Base_JSON/prefixI/json/inlg/2020.inlg-1.44.json

{
    "paper_id": "2020",
    "header": {
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        "date_generated": "2023-01-19T07:27:58.701590Z"
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    "title": "ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis",
    "authors": [
        {
            "first": "Qingyun",
            "middle": [],
            "last": "Wang",
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            "affiliation": {
                "laboratory": "",
                "institution": "University of Illinois at Urbana-Champaign",
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            "email": ""
        },
        {
            "first": "Qi",
            "middle": [],
            "last": "Zeng",
            "suffix": "",
            "affiliation": {
                "laboratory": "",
                "institution": "University of Illinois at Urbana-Champaign",
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            "email": ""
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        {
            "first": "Lifu",
            "middle": [],
            "last": "Huang",
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            "affiliation": {
                "laboratory": "",
                "institution": "University of Illinois at Urbana-Champaign",
                "location": {}
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            "email": ""
        },
        {
            "first": "Kevin",
            "middle": [],
            "last": "Knight",
            "suffix": "",
            "affiliation": {},
            "email": "kevinknight@didiglobal.com"
        },
        {
            "first": "Heng",
            "middle": [],
            "last": "Ji",
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            "affiliation": {
                "laboratory": "",
                "institution": "University of Illinois at Urbana-Champaign",
                "location": {}
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            "email": "hengji@illinois.edu"
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        {
            "first": "Nazneen",
            "middle": [
                "Fatema"
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            "last": "Rajani",
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            "affiliation": {
                "laboratory": "Salesforce Research {qingyun4",
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            "email": "nazneen.rajani@salesforce.com"
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    "abstract": "To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domainspecific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than humanwritten ones for 20% of the time. Thus, Re-viewRobot can serve as an assistant for paper reviewers, program chairs and authors. 1",
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        "abstract": [
            {
                "text": "To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domainspecific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than humanwritten ones for 20% of the time. Thus, Re-viewRobot can serve as an assistant for paper reviewers, program chairs and authors. 1",
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                "section": "Abstract",
                "sec_num": null
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            {
                "text": "As the number of papers in our field increases exponentially, the reviewing practices today are more challenging than ever. The quality of peer paper reviews is well-debated across the academic community (Bornmann et al., 2010; Mani, 2011; Sculley et al., 2018; Lipton and Steinhardt, 2019) . How Figure 1 : ReviewRobot Architecture Overview many times do we complain about a bad, random, dismissive, unfair, biased or inconsistent peer review? Authors even created various social groups at social media to release their frustrations and anger, such as the \"Reviewer #2 must be stopped\" group at Facebook 2 . How many times are our papers rejected by a conference and then accepted by a better venue with only few changes? As the number of paper submissions continues to double or even triple every year, so does the need for highquality peer reviews.",
                "cite_spans": [
                    {
                        "start": 204,
                        "end": 227,
                        "text": "(Bornmann et al., 2010;",
                        "ref_id": "BIBREF4"
                    },
                    {
                        "start": 228,
                        "end": 239,
                        "text": "Mani, 2011;",
                        "ref_id": "BIBREF48"
                    },
                    {
                        "start": 240,
                        "end": 261,
                        "text": "Sculley et al., 2018;",
                        "ref_id": "BIBREF60"
                    },
                    {
                        "start": 262,
                        "end": 290,
                        "text": "Lipton and Steinhardt, 2019)",
                        "ref_id": "BIBREF40"
                    }
                ],
                "ref_spans": [
                    {
                        "start": 297,
                        "end": 305,
                        "text": "Figure 1",
                        "ref_id": null
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                ],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "The following are two different reviews for the same paper rejected by ACL2019 and accepted by EMNLP2019 without any change on content:",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "\u2022 ACL 2019: Idea is too simple and tricky.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "\u2022 EMNLP 2019: The main strengths of the paper lie in the interesting, relatively underresearched problem it covers, the novel and valid method and the experimental results.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
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                "text": "These reviews, including the positive ones, are too vague and generic to be helpful. We often see review comments stating a paper is missing references without pointing to any specific references, or criticizing an idea is not novel without showing These findings highlight the subjectivity in human reviews and call for ReviewRobot, an automatic review assistant to help human reviewers generate knowledgeable and explainable review scores and comments, along with detailed evidence. We start by installing a brain for ReviewRobot with a large-scale background knowledge graph (KG) constructed from previous papers in the target domain using domain-specific Information Extraction (IE) techniques. For each current paper under review, we apply the same IE method to construct two KGs, from its related work section and its other sections. By comparing the differences among these KGs, we extract pieces of evidence (e.g., novel knowledge subgraphs which are in the current paper but not in background KGs) for each review category and use them to predict review scores. We manually select constructive human review sentences and generalize them into templates for each category. Then we apply these templates to convert structured evidence to natural language comments for each category, using the predicted scores as a controlling factor.",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "Experimental results show that our review score predictor reaches 71.4% overall accuracy on overall recommendation, which is very close to interhuman agreement (72.2%). The score predictor achieves 100% accuracy for both appropriateness and impact categories. Human assessment by domain experts shows that up to 70.5% of the comments generated by ReviewRobot are valid, and better than human-written ones 20% of the time.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "In summary, the major contributions of this paper are as follows:",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "\u2022 We propose a new research problem of generating paper reviews and present the first complete end-to-end framework to generate scores and comments for each review category.",
                "cite_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "\u2022 Our framework is knowledge-driven, based on fine-grained knowledge element comparison among papers, and thus the comments are highly explainable and constructive, supported by detailed evidence.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "\u2022 We create a new benchmark that includes 8K paper and review pairs, 473 manually selected pairs of paper sentences and constructive human review sentences, and a background KG constructed from 174K papers. ",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Introduction",
                "sec_num": "1"
            },
            {
                "text": "\u2022 The number of knowledge elements that appear in the contribution claims in the introduction section and that are verified in the experiment section \u2022 Abstract",
                "cite_spans": [],
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                "eq_spans": [],
                "section": "Soundness",
                "sec_num": null
            },
            {
                "text": "\u2022 attention-over-attention reader, n-best re-ranking strategy is verified in the related work section",
                "cite_spans": [],
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                "section": "Soundness",
                "sec_num": null
            },
            {
                "text": "\u2022 The number of papers about relevant knowledge elements which are missed in the related work section: In this paper, we present a simple but novel model called attention-over-attention reader for better solving cloze-style reading comprehension task. (Cui et al., 2017) The paper describes a new method called attention-over-attention for reading comprehension.",
                "cite_spans": [
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                        "start": 252,
                        "end": 270,
                        "text": "(Cui et al., 2017)",
                        "ref_id": "BIBREF13"
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                "section": "Meaningful Comparison",
                "sec_num": null
            },
            {
                "text": "(GB \u2229 GP \u03c4 ) \u2212\u1e20P \u03c4 \u2022",
                "cite_spans": [],
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                "section": "Meaningful Comparison",
                "sec_num": null
            },
            {
                "text": "The paper presents a new framework to solve the SR problem -amortized MAP inference and adopts a pre-learned affine projection layer to ensure the output is consistent with LR. (S\u00f8nderby et al., 2017) It introduces a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input.",
                "cite_spans": [
                    {
                        "start": 177,
                        "end": 200,
                        "text": "(S\u00f8nderby et al., 2017)",
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                ],
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                "section": "Novelty 33",
                "sec_num": null
            },
            {
                "text": "In high dimensions we empirically found that the GAN based approach, AffGAN produced the most visually appealing results. (S\u00f8nderby et al., 2017) Combined with GAN, this framework can obtain plausible and good results.",
                "cite_spans": [
                    {
                        "start": 122,
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                        "text": "(S\u00f8nderby et al., 2017)",
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                "section": "Soundness 174",
                "sec_num": null
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            {
                "text": "As a concrete instantiation, we show in this paper that we can enable recursive neural programs in the NPI model, and thus enable perfectly generalizable neural programs for tasks such as sorting where the original, non-recursive NPI program fails. (Cai et al., 2017) This paper improves significantly upon the original NPI work, showing that the model generalizes far better when trained on traces in recursive form.",
                "cite_spans": [
                    {
                        "start": 249,
                        "end": 267,
                        "text": "(Cai et al., 2017)",
                        "ref_id": "BIBREF7"
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                ],
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                "section": "Meaningful Comparison 16",
                "sec_num": null
            },
            {
                "text": "Potential Impact",
                "cite_spans": [],
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                "section": "Meaningful Comparison 16",
                "sec_num": null
            },
            {
                "text": "Since there may be several rounds of questioning and reasoning, these requirements bring the problem closer to task-oriented dialog and represent a significant increase in the difficulty of the challenge over the original bAbI (supporting fact) problems. (Guo et al., 2017) I am a bit worried that the tasks may be too easy (as the bAbI tasks have been), but still, I think locally these will be useful. ",
                "cite_spans": [
                    {
                        "start": 255,
                        "end": 273,
                        "text": "(Guo et al., 2017)",
                        "ref_id": "BIBREF20"
                    }
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                "section": "14",
                "sec_num": null
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            {
                "text": "Generating meaningful and explainable reviews requires ReviewRobot to understand the knowledge elements of each paper. We apply a state-of-theart Information Extraction (IE) system for Natural Language Processing (NLP) and Machine Learning (ML) domains (Luan et al., 2018) to construct the following knowledge graphs (KGs):",
                "cite_spans": [
                    {
                        "start": 253,
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                        "text": "(Luan et al., 2018)",
                        "ref_id": "BIBREF45"
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                "section": "Knowledge Graph Construction",
                "sec_num": "2.2"
            },
            {
                "text": "\u2022 G P\u03c4 : A KG constructed from the abstract and conclusion sections of a target paper under review P \u03c4 , which describes the main techniques. \u2022\u1e20 P\u03c4 : A KG constructed from the related work section of P \u03c4 , which describes related techniques. \u2022 G B : A background KG constructed from all of the old NLP/ML papers published before the publication year of P \u03c4 , in order to teach ReviewRobot what's happening in the field.",
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                "section": "Knowledge Graph Construction",
                "sec_num": "2.2"
            },
            {
                "text": "Each node v \u2208 V in a KG represents an entity, namely a cluster of co-referential entity mentions, assigned one of six types: Task, Method, Evaluation Metric, Material, Other Scientific Terms, and Generic Terms. Following the previous work on entity coreference for scientific domains (Koncel-Kedziorski et al., 2019), we choose the longest informative entity mention in each cluster to represent the entity. We consider two entity clusters from different papers as coreferential if one's representative mention appears in the other. Each edge represents a relation between two entities. There are seven relation types: Used-for, Featureof, Evaluate-for, Hyponym-of, Part-of, Compare, and Conjunction. Figure 2 shows an example KG constructed from (Bahdanau et al., 2015) .",
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                        "text": "(Bahdanau et al., 2015)",
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                        "text": "Figure 2",
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                "section": "Knowledge Graph Construction",
                "sec_num": "2.2"
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            {
                "text": "We compare the differences among the constructed KGs to extract evidence for each review category. Table 1 shows the methods to extract evidence and some examples for each category.",
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                    {
                        "start": 99,
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                        "text": "Table 1",
                        "ref_id": "TABREF2"
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                "section": "Evidence Extraction",
                "sec_num": "2.3"
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            {
                "text": "Following (Kang et al., 2018) , we consider review score prediction as a multi-label classification task. For a target paper, we first encode its category related sentences with an attentional Gated Recurrent Unit (GRU) (Cho et al., 2014; Bahdanau et al., 2015) to obtain attentional contextual sentence em-bedding. We also encode the extracted evidence for each review category with an embedding layer. Then we concatenate the context embedding and evidence embedding to predict the quality score r in the range of 1 to 5 with a linear output layer. We use the prediction probability as the confidence score.",
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                        "text": "(Kang et al., 2018)",
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                        "text": "(Cho et al., 2014;",
                        "ref_id": "BIBREF12"
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                        "start": 239,
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                        "text": "Bahdanau et al., 2015)",
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                "section": "Score Prediction",
                "sec_num": "2.4"
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            {
                "text": "Given the evidence graphs and predicted scores as input, we perform template-based comment generation for each category. We aim to learn good templates from human reviews. Unfortunately as we have discussed earlier, not all human written review sentences are of high quality, even for those accepted papers. Therefore in order to generalize templates, we need to carefully select those constructive and informative human review sentences that are supported by certain evidence in the papers. To avoid expensive manual selection, we design a semi-automatic bootstrapping approach. We manually annotate 200 paper-review pairs from ACL2017 and ICIR2017 datasets, and then use them as seed annotations to train an attentional GRU (Cho et al., 2014) based binary (select/not select) classifier to process the remaining human review sentences and keep high-quality reviews with high confidence. Our attentional GRU achieves binary classification accuracy 85.25%. Table 2 shows the annotation statistics and some examples.",
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                        "text": "(Cho et al., 2014)",
                        "ref_id": "BIBREF12"
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                        "text": "Table 2",
                        "ref_id": "TABREF3"
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                "section": "Comment Generation",
                "sec_num": "2.5"
            },
            {
                "text": "For appropriateness, soundness, and potential impact categories, we generate generic positive or negative comments based on the predicted scores. For summary, novelty, and meaningful comparison categories, we consider review generation as a template-based graph-to-text generation task. Specifically, for summary and novelty, we generate reviews by describing the Used-for, Feature-of, Compare and Evaluate-for relations in evidence graphs. We choose positive or negative templates depending on whether the predicted scores are above 3. We use the predicted overall recommendation score to control summary generation. For related work, we keep the knowledge elements in the evidence graph with a TF-IDF score (Jones, 1972) higher than 0.5. For each knowledge element, we recommend the most recent 5 papers that are not cited as related papers.",
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                        "text": "(Jones, 1972)",
                        "ref_id": "BIBREF27"
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                "section": "Comment Generation",
                "sec_num": "2.5"
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            {
                "text": "We choose papers in NLP and ML domains in our experiments because it's easy for us to analyze results, and we are not the most harsh community in Computer Science: the average review score in our corpus is 3.3 out of 5 while it is 2.5/5 in the computer system area (Anderson, 2009) . In addition to the review corpus constructed by (Kang et al., 2018) , we have collected additional paperreview pairs from openreview 4 and NeurIPS 5 . In total, we have collected 8,110 paper and review pairs as shown in Table 3 . We construct the background KG from 174,165 papers from the open research corpus . Table 4 shows the data statistics of background KGs. ",
                "cite_spans": [
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                        "text": "(Anderson, 2009)",
                        "ref_id": "BIBREF1"
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                        "text": "(Kang et al., 2018)",
                        "ref_id": "BIBREF31"
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                        "text": "Table 3",
                        "ref_id": "TABREF4"
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                        "text": "Table 4",
                        "ref_id": "TABREF5"
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                "section": "Data",
                "sec_num": "3.1"
            },
            {
                "text": "We use the ACL2017 dataset in the score prediction task because it has complete score annotations for each review category. We follow the data split of PeerRead (Kang et al., 2018) 6 . Unlike PeerRead which uses multiple review scores for the same input paper, we use the rounded average score of each category as the target score. Table 5 shows that our model trained from carefully selected constructed reviews has already reached a prediction accuracy of 71.43% for overall recommendation, which is very close to the human inter-annotator agreement Conference  Year  2013  2014  2015  2016  2017  2018  2019  2020   ICLR  ----404  874  1,342  2,067  NeurIPS  342  399  389  545  655  963  --ACL  ----130 --- (72.2%) and dramatically advances state-of-the-art approaches in most categories. Our model also produces the lowest mean square errors for all categories.",
                "cite_spans": [
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                        "start": 161,
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                        "text": "(Kang et al., 2018) 6",
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                        "start": 332,
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                        "text": "Table 5",
                        "ref_id": "TABREF6"
                    },
                    {
                        "start": 552,
                        "end": 706,
                        "text": "Conference  Year  2013  2014  2015  2016  2017  2018  2019  2020   ICLR  ----404  874  1,342  2,067  NeurIPS  342  399  389  545  655  963  --ACL  ----130",
                        "ref_id": "TABREF2"
                    }
                ],
                "eq_spans": [],
                "section": "Score Prediction Performance",
                "sec_num": "3.2"
            },
            {
                "text": "Our knowledge graph synthesis based approach is particularly effective at predicting Novelty score and achieves the accuracy of 71.43%, which is much higher than the accuracy (28.57%) of all other automatic prediction methods using paper abstracts only as input. In Figure 3 we show the average number of new knowledge elements of our test set consisting of ACL2017 papers, when it's reviewed during different years. When the background KG includes newer work, the novelty of these papers decreases, especially after 2017. This indicates that our approach provides a reliable measure for computing novelty.",
                "cite_spans": [],
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                    {
                        "start": 266,
                        "end": 274,
                        "text": "Figure 3",
                        "ref_id": "FIGREF0"
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                ],
                "eq_spans": [],
                "section": "Score Prediction Performance",
                "sec_num": "3.2"
            },
            {
                "text": "As a fun experiment, we also run ReviewRobot on this paper submission itself. The predicted review scores are 5, 3, 4, 3, 4, 4, and 4 for Appropriateness, Meaningful Comparison, Soundness, Novelty, Clarity, Potential Impact and Overall Recommendation, respectively, which means this paper is likely to be accepted.",
                "cite_spans": [],
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                "section": "Score Prediction Performance",
                "sec_num": "3.2"
            },
            {
                "text": "For the system generated review comments for 50 ACL2017 papers, we ask domain experts to check whether each comment is constructive and valid. Two researchers independently annotate the reviews and reach the inter-annotator agreement of 92%, 92%, and 82% for Novelty, Summary and Related Work, respectively. One expert annotator performs data adjudication. The percentages of constructive and valid comments are 70.5%, 44.6% and 41.7% for Summary, Novelty and Meaningful Comparison, respectively. Human assessors also find that for 20% of these papers, human reviewers do not suggest missing related work for Meaningful Comparison, while ReviewRobot generates constructive and informative comments. For example, the human reviewer states \"The paper would be stronger with the inclusion of more baselines based on related work 7 \", but fails to provide any useful references. In the following we compare the human and system generated reviews for an example paper (Niu et al., 2017) ",
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                        "end": 981,
                        "text": "(Niu et al., 2017)",
                        "ref_id": "BIBREF55"
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                "eq_spans": [],
                "section": "Comment Generation Performance",
                "sec_num": "3.3"
            },
            {
                "text": "The quality of ReviewRobot is mainly limited by state-of-the-art Information Extraction performance for the scientific literature domain. In the future we plan to annotate more data to cover more dimensions for paper profiling (such as goal and main contribution), and more fine-grained knowledge types to improve the extraction quality. For example, for the NLP domain we can extract finergrained subtypes: a model can include parameters, components and features. The goal of an NLP paper could belong to: \"New methods for specific NLP problems\", \"End-user applications\", \"Corpora and evaluations\", \"New machine learning methods for NLP\", \"Linguistic theories \", \"Cognitive modeling and psycholinguistic research\" or \"Applications to social sciences and humanities\". Our current evidence extraction framework also lacks of a salience measure to assign different weights to different types of knowledge elements.",
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                "section": "Remaining Challenges and Limitations",
                "sec_num": "3.4"
            },
            {
                "text": "Paper review generation requires background knowledge acquisition and comparison with the target paper content. Our novel approach on constructing background KG has helped improve the quality of review comments on novelty but the KG is still too flat to generate comments on soundness. For example, from the following two sentences in a paper: \"Third, at least 93% of time expressions contain at least one time token.\", and \"For the relaxed match on all three datasets , SynTime-I and SynTime-E achieve recalls above 92%.\", a knowledgeable human reviewer can infer 93% as the upper bound of performance and write a comment: \"Section 5.2 : given this approach is close to the ceiling of performance since 93 % expressions contain time token , and the system has achieved 92 % recall , how do you plan to improve further?\". Similarly, ReviewRobot cannot generalize knowledge elements into high-level comments such as \"deter-ministic\" as in \"The tasks 1-5 are also completely deterministic\".",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Remaining Challenges and Limitations",
                "sec_num": "3.4"
            },
            {
                "text": "ReviewRobot still lacks of deep knowledge reasoning ability to judge the soundness of algorithm design details, such as whether the split of data set makes sense, whether a model is able to generalize. ReviewRobot is not able to comment on missing hypotheses, the problems on experimental setting and future work. ReviewRobot currently focuses on text only and cannot comment on mathematical formulas, tables and figures.",
                "cite_spans": [],
                "ref_spans": [],
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                "section": "Remaining Challenges and Limitations",
                "sec_num": "3.4"
            },
            {
                "text": "Good machine learning models rely on good data. We need massive amounts of good human reviews to fuel ReviewRobot. In our current approach, we manually select a subset of good human review sentences that are also supported by corresponding sentences in the target papers. This process is very time-consuming and expensive. We need to build a better review infrastructure in our community, e.g., asking authors to provide feedback and rating to select constructive reviews as in NAACL2018 8 .",
                "cite_spans": [],
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                "section": "Remaining Challenges and Limitations",
                "sec_num": "3.4"
            },
            {
                "text": "Paper Acceptance Prediction. Kang et al. (2018) has constructed a paper review corpus, PeerRead, and trained paper acceptance classifiers. Huang (2018) applies an interesting visual feature to compare the pdf layouts and proves its effectiveness to make paper acceptance decision. Ghosal et al. (2019) applies sentiment analysis features to improve acceptance prediction. The KDD2014 PC chairs exploit author status and review comments for predicting paper acceptance (Leskovec and Wang, 2014) . We extend these methods to score prediction and comment generation with detailed knowledge element level evidence for each specific review category. Paper Review Generation. Bartoli et al. (2016) proposes the first deep neural network framework to generate paper review comments. The generator is trained with 48 papers from their own lab. In comparison, we perform more concrete and explainable review generation by predicting scores and generating comments for each review category following a rich set of evidence, and use a much larger data set. Nagata (2019) generates comment sentences to explain grammatical errors as feedback to improve paper writing. (Xing et al., 2020; Luu et al., 2020) extract paper-paper relations and Review Generation in other Domains. Automatic review generation techniques have been applied to many other domains including music (Tata and Di Eugenio, 2010) , restaurants (Oraby et al., 2017; Juuti et al., 2018; Li et al., 2019a; Bra\u017einskas et al., 2020) , and products (Catherine and Cohen, 2018; Li et al., 2019a; Li and Tuzhilin, 2019; Dong et al., 2017; Ni and McAuley, 2018; Bra\u017einskas et al., 2020) . These methods generally apply a sequence-to-sequence model with attention to aspects and attributes (e.g. food type). Compared to these domains, paper review generation is much more challenging because it requires the model to perform deep understanding on paper content, construct knowledge graphs to compare knowledge elements across sections and papers, and synthesize information as input evidence for comment generation.",
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                        "start": 29,
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                        "text": "Ghosal et al. (2019)",
                        "ref_id": "BIBREF17"
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                "section": "Related Work",
                "sec_num": "4"
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            {
                "text": "Controlled Knowledge-Driven Generation. There have been some other studies on text generation controlled by sentiment , topic (Krishna and Srinivasan, 2018 ), text style (Shen et al., 2017; Liu et al., 2019a; Tikhonov et al., 2019) , and facts . The usage of external supportive knowledge in text generation can be roughly divided into the following three levels: (1) Knowledge Description, which transforms structured data into unstructured text, such as Table- to-Text Generation (Mei et al., 2016; Lebret et al., 2016; Chisholm et al., 2017; Sha et al., 2018; Liu et al., 2018b; Nema et al., 2018; Wang et al., 2018a; Moryossef et al., 2019; Nie et al., 2019; Castro Ferreira et al., 2019; Shahidi et al., 2020) and its variants in low-resource (Ma et al., 2019) and multi-lingual setting (Kaffee et al., 2018a,b) , Data-to-Document (Wiseman et al., 2017; Puduppully et al., 2019; Gong et al., 2019; Iso et al., 2019) , Graph-to-Text (Flanigan et al., 2016; Song et al., 2018; Zhu et al., 2019; Koncel-Kedziorski et al., 2019) , and Topic-to-text (Tang et al., 2019) , and Knowledge Base Description (Kiddon et al., 2016; Gardent et al., 2017; Koncel-Kedziorski et al., 2019) ; (2) Knowledge Synthesis, which retrieves knowledge base and organizes text answers, such as Video Caption Generation (Whitehead et al., 2018) , KB-supported Dialogue Generation (Han et al., 2015; Parthasarathi and Pineau, 2018; Liu et al., 2018a; Wen et al., 2018; Chen et al., 2019; Liu et al., 2019b) , Knowledge-guided comment Generation (Li et al., 2019b) , paper generation Cachola et al., 2020) , and abstractive summarization (Gu et al., 2016; Sharma et al., 2019; Huang et al., 2020) .",
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                        "start": 126,
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                        "start": 170,
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                        "text": "(Shen et al., 2017;",
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                        "start": 190,
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                        "ref_id": "BIBREF42"
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                        "start": 209,
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                        "ref_id": "BIBREF49"
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                        "start": 545,
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                        "text": "Sha et al., 2018;",
                        "ref_id": "BIBREF61"
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                        "text": "Castro Ferreira et al., 2019;",
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                        "text": "Cachola et al., 2020)",
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                        "ref_id": "BIBREF19"
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                "ref_spans": [
                    {
                        "start": 456,
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                "section": "Related Work",
                "sec_num": "4"
            },
            {
                "text": "The types of evidence we have designed in this paper are limited to NLP, ML or related areas, and thus they are not applicable to other scientific domains such as biomedical science and chemistry. Whether ReviewRobot is essentially beneficial to the scientific community also depends on who uses it. Here are some example scenarios where Re-viewRobot should and should not be used:",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Do: Reviewers use ReviewRobot merely as an assistant to write more constructive comments and compare notes.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Do: Editors use ReviewRobot to assist filtering very bad papers during screening.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Do: Authors use ReviewRobot to get initial feedback to improve paper writing such as adding missing references and highlighting the recommended novel points.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Do: Researchers use ReviewRobot to perform literature survey, find more good papers and validate the novelty of their papers.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Not-Do: Reviewers submit Re-viewRobot's output without reading the paper carefully.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Not-Do: Editors send ReviewRobot's output and make decisions based on it.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "\u2022 Should-Not-Do: Authors revise their papers to fit into ReviewRobot's features to boost review scores. For example, authors should not deliberately cite all related papers or add irrelevant new terms to boost their review scores.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Application Limitations and Ethical Statement",
                "sec_num": "5"
            },
            {
                "text": "We build a ReviewRobot for predicting review scores and generating detailed comments for each review category, which can serve as an effective assistant for human reviewers and authors who want to polish their papers. The key innovation of our approach is to construct knowledge graphs from the target paper and a large collection of in-domain background papers, and summarize the pros and cons of each paper on knowledge element level with detailed evidence. We plan to enhance Re-viewRobot's knowledge reasoning capability by building a taxonomy on top of the background KG, and incorporating multi-modal analysis of formulas, tables, figures, and citation networks.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Conclusions and Future Work",
                "sec_num": "6"
            },
            {
                "text": "https://www.facebook.com/groups/reviewer2/",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "We collect ICLR paper using open review API https: //openreview-py.readthedocs.io/ 5 https://papers.nips.cc/6 We exclude the training pairs that we fail to run IE system on. The test set remains the same as(Kang et al., 2018).",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "Review for(Niu et al., 2017) https://github. com/allenai/PeerRead/blob/master/data/ acl_2017/train/reviews/318.json",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            },
            {
                "text": "https://naacl2018.wordpress.com/2018/02/26/acceptanceand-author-feedback/ use them to guide citation text generation.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "",
                "sec_num": null
            }
        ],
        "back_matter": [
            {
                "text": "The knowledge extraction and prediction components were supported by the U.S. NSF No. 1741634 and Air Force No. FA8650-17-C-7715. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.",
                "cite_spans": [],
                "ref_spans": [],
                "eq_spans": [],
                "section": "Acknowledgments",
                "sec_num": null
            }
        ],
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            "FIGREF0": {
                "type_str": "figure",
                "num": null,
                "uris": null,
                "text": "The average number of new knowledge elements in ACL2017 test papers given the background KG constructed from (1965\u223ccutoff year)"
            },
            "TABREF1": {
                "content": "<table><tr><td/><td/><td/><td>Neural architecture</td></tr><tr><td/><td/><td/><td>Used for</td></tr><tr><td/><td/><td/><td>Cloze-style reading</td></tr><tr><td/><td/><td/><td>comprehension problem</td></tr><tr><td/><td/><td>Used for</td></tr><tr><td/><td/><td>Large-scale training data</td><td>Attention mechanism</td></tr><tr><td/><td/><td colspan=\"2\">(Bahdanau et al., 2015; Hermann</td></tr><tr><td/><td/><td>et al., 2015)</td></tr><tr><td>Potential</td><td>\u2022 The number of new knowledge elements in the</td><td colspan=\"2\">\u2022 5 new knowledge elements</td></tr><tr><td>Impact</td><td>future work section</td><td colspan=\"2\">\u2022 1 new architecture</td></tr><tr><td/><td>\u2022 The number of new software, systems, data sets,</td><td/></tr><tr><td/><td>and other resources</td><td/></tr><tr><td>Overall Recom-</td><td>\u2022 All features mentioned in the above categories</td><td/></tr><tr><td>mendations</td><td>\u2022 Abstract</td><td/></tr></table>",
                "text": "The number of papers about relevant knowledge elements which are claimed new in the related work section: GB \u2229 GP \u03c4 \u2229\u1e20P \u03c4 \u2022 The description sentences about comparison with related work \u2022 If the related work section is not available, we use the difference between GP \u03c4 and GB instead",
                "type_str": "table",
                "html": null,
                "num": null
            },
            "TABREF2": {
                "content": "<table><tr><td colspan=\"2\">: Evidence Extraction for the example paper Attention-over-Attention Neural Networks for Reading Com-</td></tr><tr><td>prehension (Cui et al., 2017)</td><td/></tr><tr><td>2 Approach</td><td>2018):</td></tr><tr><td>2.1 Overview</td><td>\u2022 Summary: What is this paper about?</td></tr><tr><td>Figure 1 illustrates the overall architecture of Re-viewRobot. ReviewRobot first constructs knowl-edge graphs (KGs) for each target paper and a large collection of background papers, then it extracts evidence by comparing knowledge elements across multiple sections and papers, and uses the evidence to predict scores and generate comments for each review category. We adopt the following most common categories from NeurIPS2019 3 and PeerRead (Kang et al.,</td><td>\u2022 Appropriateness: Does the paper fit in the venue? \u2022 Clarity: Is it clear what was done and why? Is the paper well-written and well-structured? \u2022 Novelty: Does this paper break new ground in topic, methodology, or content? How exciting and innovative is the research it describes? \u2022 Soundness: Can one trust the empirical claims of the paper -are they supported by proper experiments and are the results of the experiments correctly interpreted?</td></tr><tr><td>3 https://nips.cc/Conferences/2019/</td><td>\u2022 Meaningful Comparison: Do the authors</td></tr><tr><td>PaperInformation/ReviewerGuidelines</td><td>make clear where the problems and methods</td></tr></table>",
                "text": "",
                "type_str": "table",
                "html": null,
                "num": null
            },
            "TABREF3": {
                "content": "<table/>",
                "text": "",
                "type_str": "table",
                "html": null,
                "num": null
            },
            "TABREF4": {
                "content": "<table><tr><td>Years (1965\u223c)</td><td>2011</td><td>2012</td><td>2013</td><td>2014</td><td>2015</td><td>2016</td><td>2017</td><td/><td>2019</td></tr><tr><td># of Entities</td><td>535,075</td><td>585,321</td><td>628,713</td><td>683,686</td><td>737,878</td><td>801,740</td><td>870,992</td><td colspan=\"2\">950,457 1,008,955</td></tr><tr><td># of Relations</td><td>160,123</td><td>175,780</td><td>188,876</td><td>205,898</td><td>222,592</td><td>242,312</td><td>263,827</td><td>288,805</td><td>307,636</td></tr></table>",
                "text": "Data Statistics for Paper Review Corpus (# of papers)",
                "type_str": "table",
                "html": null,
                "num": null
            },
            "TABREF5": {
                "content": "<table><tr><td>Category</td><td>Human Kappa Score</td><td>Human Average Inter Annotator Agreement</td><td colspan=\"2\">CNN (Kang et al., 2018) Score Acc. Decision Acc. MSE</td><td colspan=\"2\">GRU with Abstract Score Acc. Decision Acc. MSE</td><td colspan=\"2\">GRU with Evidence Score Acc. Decision Acc. MSE</td></tr><tr><td colspan=\"2\">Recommendation 33.63</td><td>72.2</td><td>71.43 57.14</td><td>0.714</td><td>71.43 57.14</td><td colspan=\"2\">0.714 85.71 71.43</td><td>0.571</td></tr><tr><td colspan=\"2\">Appropriateness 100</td><td>100</td><td>85.71 100</td><td>0.143</td><td>85.71 100</td><td colspan=\"2\">0.143 85.71 100</td><td>0.143</td></tr><tr><td>Meaningful Comparison</td><td>100</td><td>100</td><td>57.14 57.14</td><td>0.857</td><td>57.14 71.42</td><td colspan=\"2\">0.857 71.43 71.43</td><td>0.714</td></tr><tr><td>Soundness</td><td>100</td><td>100</td><td>42.86 42.86</td><td>1.86</td><td>14.28 85.71</td><td colspan=\"2\">0.857 71.43 85.71</td><td>0.714</td></tr><tr><td>Novelty</td><td>100</td><td>100</td><td>42.86 42.86</td><td>2.29</td><td>28.57 28.57</td><td colspan=\"2\">2.43 71.43 71.43</td><td>0.714</td></tr><tr><td>Clarity</td><td>70.20</td><td>86.11</td><td>42.86 71.43</td><td>1.00</td><td>42.86 71.43</td><td colspan=\"2\">1.00 42.86 71.43</td><td>1.00</td></tr><tr><td>Potential Impact</td><td>100</td><td>100</td><td>85.71 100</td><td>0.143</td><td>85.71 100</td><td colspan=\"2\">0.571 85.71 100</td><td>0.143</td></tr></table>",
                "text": "Data Statistics for Background Knowledge Graphs since 1965",
                "type_str": "table",
                "html": null,
                "num": null
            },
            "TABREF6": {
                "content": "<table/>",
                "text": "",
                "type_str": "table",
                "html": null,
                "num": null
            },
            "TABREF7": {
                "content": "<table><tr><td>* [SYSTEM] Novelty</td><td>Gabriel Stanovsky, Ido Dagan, et al. 2015. Open IE as an Intermediate Struc-ture for Semantic Tasks. In Proceedings of the 53rd Annual Meeting of the Associ-ation for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 303-308.</td></tr><tr><td>* [SYSTEM] The paper proposes novel attention</td><td/></tr><tr><td>scheme and sememe-encoded models.</td><td>* [HUMAN] The paper would be stronger with</td></tr><tr><td>* [HUMAN] I see the main contribution of the</td><td>the inclusion of more baselines based on related</td></tr><tr><td>work to be the results which show that we can</td><td>work.</td></tr><tr><td>learn better word representations (unsure about</td><td/></tr><tr><td>wsi) by modeling sememe information than other</td><td/></tr><tr><td>competitive baselines.</td><td/></tr><tr><td>Meaningful Comparison</td><td/></tr><tr><td>* [SYSTEM] The following related papers are</td><td/></tr><tr><td>missing:</td><td/></tr><tr><td>1. About low-dimensional semantic space:</td><td/></tr><tr><td>(a) Unsupervised approximate-semantic vo-</td><td/></tr><tr><td>cabulary learning for human action and</td><td/></tr><tr><td>video classification (Zhao and Ip, 2013)</td><td/></tr><tr><td>Qiong Zhao and Horace HS Ip. 2013.</td><td/></tr><tr><td>Unsupervised Approximate-semantic Vo-</td><td/></tr><tr><td>cabulary Learning for Human Action</td><td/></tr><tr><td>and Video Classification. Pattern Recog-</td><td/></tr><tr><td>nition Letters, 34(15):1870-1878.</td><td/></tr><tr><td>2. About sememes:</td><td/></tr><tr><td>(a) Chinese Word Sense Disambiguation</td><td/></tr><tr><td>with PageRank and HowNet (Wang et al.,</td><td/></tr><tr><td>2008): Jinghua Wang, Jianyi Liu, and</td><td/></tr><tr><td>Ping Zhang. 2008. Chinese Word</td><td/></tr><tr><td>Sense Disambiguation with PageRank</td><td/></tr><tr><td>and HowNet. In Proceedings of the</td><td/></tr><tr><td>Sixth SIGHAN Workshop on Chinese</td><td/></tr><tr><td>Language Processing.</td><td/></tr><tr><td>(b) A maximum entropy approach to</td><td/></tr><tr><td>HowNet-based Chinese word sense</td><td/></tr><tr><td>disambiguation (Wong and Yang, 2002):</td><td/></tr><tr><td>Ping Wai Wong and Yongsheng Yang.</td><td/></tr><tr><td>2002. A Maximum Entropy Approach</td><td/></tr><tr><td>to HowNet-based Chinese Word Sense</td><td/></tr><tr><td>Disambiguation. In COLING-02: SE-</td><td/></tr><tr><td>MANET: Building and Using Semantic</td><td/></tr><tr><td>Networks.</td><td>:</td></tr><tr><td>3. About word similarity and word analogy:</td><td>Summary</td></tr><tr><td>(a) Open IE as an Intermediate Structure for</td><td/></tr><tr><td>Semantic Tasks (Stanovsky et al., 2015):</td><td/></tr></table>",
                "text": "The paper proposes novel skipgram, attention scheme, sememe-encoded models and word representation learning for NLP tasks. The authors uses linguistic common-sense knowledge bases.*[HUMAN]  This paper proposes the use of HowNet to enrich embeddings.",
                "type_str": "table",
                "html": null,
                "num": null
            }
        }
    }
}