test

app.py

@@ -5,13 +5,12 @@ from reference_string_parsing import *
 from controlled_summarization import *
 from dataset_extraction import *
 
-from controlled_summarization import recommended_kw
 import requests
 
 # Example Usage
-# url = "https://arxiv.org/pdf/2305.14996.pdf"
-# dest_folder = "./examples/"
-# download_pdf(url, dest_folder)
+#url = "https://arxiv.org/pdf/2305.14996.pdf"
+#dest_folder = "./examples/"
+#download_pdf(url, dest_folder)
 
 
 with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
@@ -29,23 +28,20 @@ with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
                         ctrlsum_file = gr.File(label="Input File")
                         ctrlsum_str = gr.TextArea(label="Input String", max_lines=5)
                         with gr.Column():
-                            gr.Markdown("* Set the length of text used for summarization. Length 0 will exert no control over length.")
+                            gr.Markdown("* Length 0 will exert no control over length.")
                             # ctrlsum_file_beams = gr.Number(label="Number of beams for beam search", value=1, precision=0)
                             # ctrlsum_file_sequences = gr.Number(label="Number of generated summaries", value=1, precision=0)
-                            ctrlsum_file_length = gr.Radio(label="Length", value=0, choices=[0, 50, 100, 200])
-                            kw = gr.Radio(visible=False)
-                            ctrlsum_file_keywords = gr.Textbox(label="Keywords", max_lines=1)
+                            ctrlsum_file_length = gr.Slider(0,300,step=50, label="Length")
+                            ctrlsum_file_keywords = gr.Textbox(label="Keywords",max_lines=1)
                         with gr.Row():
                             ctrlsum_file_btn = gr.Button("Generate")
                     ctrlsum_file_output = gr.Textbox(
                         elem_id="htext",
                         label="Summary",
                     )
-                ctrlsum_file_examples = gr.Examples(
-                    examples=[["examples/H01-1042_body.txt", 50, "automatic evaluation technique", "", ""],
-                              ["examples/H01-1042.pdf", 0, "automatic evaluation technique", "", ""]],
-                    inputs=[ctrlsum_file, ctrlsum_file_length, ctrlsum_file_keywords, ctrlsum_str, ctrlsum_url
-                            ])
+                ctrlsum_file_examples = gr.Examples(examples=[["examples/H01-1042_body.txt", 50, "automatic evaluation technique", "",""],["examples/H01-1042.pdf", 0, "automatic evaluation technique","",""]],
+                                                inputs=[ctrlsum_file, ctrlsum_file_length, ctrlsum_file_keywords, ctrlsum_str, ctrlsum_url])
+
 
 
 
@@ -55,37 +51,15 @@ with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
             outputs=[ctrlsum_file_output, ctrlsum_str, ctrlsum_file]
         )
         def clear():
-            return None, 0, None, None, gr.Radio(visible=False)
-
-
-        def update_url(url):
-            if url in recommended_kw.keys():
-                keywords = recommended_kw[url]
-                if keywords != None:
-                    return None, None, gr.Radio(choices=keywords[:3], label="Recommended Keywords", visible=True,
-                                                interactive=True)
+            return None,0,None, None
 
-            return None, None, gr.Radio(visible=False)
-
-
-        ctrlsum_file.upload(clear, inputs=None,
-                            outputs=[ctrlsum_str, ctrlsum_file_length, ctrlsum_file_keywords, ctrlsum_url, kw])
-        ctrlsum_url.input(update_url, inputs=ctrlsum_url, outputs=[ctrlsum_str, ctrlsum_file, kw])
 
+        ctrlsum_file.upload(clear, inputs=None,outputs=[ctrlsum_str,ctrlsum_file_length,ctrlsum_file_keywords, ctrlsum_url])
+        ctrlsum_url.input(clear, inputs=None, outputs=[ctrlsum_str, ctrlsum_file_length, ctrlsum_file_keywords, ctrlsum_file])
         ctrlsum_str.input(clear, inputs=None,
-                          outputs=[ctrlsum_url, ctrlsum_file_length, ctrlsum_file_keywords, ctrlsum_file, kw])
-
-
-
-        def select_kw(env: gr.SelectData):
-            return env.value
-
-
-        kw.select(select_kw, None, ctrlsum_file_keywords)
-
+                          outputs=[ctrlsum_url, ctrlsum_file_length, ctrlsum_file_keywords, ctrlsum_file])
         # Reference String Parsing
         with gr.TabItem("Reference String Parsing"):
-            gr.Markdown(rsp_title_md)
             with gr.Box():
                 gr.Markdown(rsp_str_md)
                 with gr.Row():
@@ -139,7 +113,6 @@ with gr.Blocks(css="#htext span {white-space: pre-line}") as demo:
 
         # Dataset Extraction
         with gr.TabItem("Dataset Mentions Extraction"):
-            gr.Markdown(de_title_md)
             with gr.Box():
                 gr.Markdown(de_str_md)
                 with gr.Row():

controlled_summarization.py

@@ -3,71 +3,9 @@ import torch
 from SciAssist import Summarization
 import os
 import requests
-from datasets import load_dataset
-
-print(f"Is CUDA available: {torch.cuda.is_available()}")
-# True
-if torch.cuda.is_available():
-    print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
-    device = 'gpu'
-    ctrlsum_pipeline = Summarization(os_name="nt",model_name="flan-t5-xl",checkpoint="dyxohjl666/flant5-xl-cocoscisum",device=device)
-else:
-    device = 'cpu'
-    ctrlsum_pipeline = Summarization(os_name="nt",device=device)
-
-
-acl_dict = {}
-recommended_kw = {}
-acl_data = load_dataset("dyxohjl666/CocoScisum_ACL", revision="refs/convert/parquet")
-
-
-def convert_to_dict(data):
-    """ Dict:
-        { url:
-            {length:
-                {keywords: summary};
-             raw_text:
-                 str;
-            }
-        }
-
-    """
-    url = data["url"]
-    text = data["text"]
-    keywords = data["keywords"]
-    length = data["length"]
-    summary = data["summary"]
-    for u, t, k, l, s in zip(url, text, keywords, length, summary):
-        if len(u) < 5:
-            continue
-        u = u + ".pdf"
-        if k == None:
-            k = ""
-        if l == None:
-            l = ""
-        k = str(k).strip()
-        l = str(l).strip()
-        if u in acl_dict.keys():
-            if k in acl_dict[u][l].keys():
-                continue
-            else:
-                acl_dict[u][l][k] = s
-        else:
-            acl_dict[u] = {"": {}, "50": {}, "100": {}, "200": {}, "raw_text": t}
-
-        # kws
-        if u in recommended_kw.keys():
-            if k == "" or k in recommended_kw[u]:
-                continue
-            else:
-                recommended_kw[u].append(k)
-        else:
-            recommended_kw[u] = []
-    return 1
+device = "gpu" if torch.cuda.is_available() else "cpu"
 
-
-for i in acl_data.keys():
-    signal = convert_to_dict(acl_data[i])
+ctrlsum_pipeline = Summarization(os_name="nt",checkpoint="google/flan-t5-base",device=device)
 
 
 def download_pdf(url, dest_folder):
@@ -92,15 +30,16 @@ def download_pdf(url, dest_folder):
     return filename
 
 
-def ctrlsum_for_str(input, length=None, keywords=None) -> List[Tuple[str, str]]:
+def ctrlsum_for_str(input,length=None, keywords=None) -> List[Tuple[str, str]]:
+
     if keywords is not None:
         keywords = keywords.strip().split(",")
         if keywords[0] == "":
             keywords = None
-    if length == 0 or length is None:
+    if length==0 or length is None:
         length = None
     results = ctrlsum_pipeline.predict(input, type="str",
-                                       length=length, keywords=keywords, num_beams=1)
+                                    length=length, keywords=keywords)
 
     output = []
     for res in results["summary"]:
@@ -110,49 +49,31 @@ def ctrlsum_for_str(input, length=None, keywords=None) -> List[Tuple[str, str]]:
 
 def ctrlsum_for_file(input=None, length=None, keywords="", text="", url="") -> List[Tuple[str, str, str]]:
     if input == None and url == "":
-        if text == "":
-            return None, "Input cannot be left blank.", None
+        if text=="":
+            return None,"Input cannot be left blank.",None
         else:
-            return ctrlsum_for_str(text, length, keywords), text, None
+            return ctrlsum_for_str(text,length,keywords),text, None
     else:
-        filename = ""
-        url = url.strip()
+        filename=""
         if url != "":
-            if len(url) > 4 and url[-3:] == "pdf":
-                if url.strip() in acl_dict.keys():
-                    raw_text = acl_dict[url]["raw_text"]
-                    l = str(length)
-                    if length == 0:
-                        l = ""
-                    if l in acl_dict[url].keys():
-                        if keywords.strip() in acl_dict[url][l].keys():
-                            summary = acl_dict[url][l][keywords]
-                            return summary, raw_text, None
-                    if keywords.strip() == "":
-                        keywords = None
-                    if l == "":
-                        l = None
-                    return ctrlsum_for_str(raw_text, int(l), keywords), raw_text, None
-
+            if len(url) > 4:
                 filename = download_pdf(url, './cache/')
-            else:
-                "Invalid url(Not PDF)!", None, None
         else:
             filename = input.name
         if keywords != "":
             keywords = keywords.strip().split(",")
             if keywords[0] == "":
                 keywords = None
-        if length == 0:
+        if length==0:
             length = None
         # Identify the format of input and parse reference strings
         if filename[-4:] == ".txt":
             results = ctrlsum_pipeline.predict(filename, type="txt",
-                                               save_results=False,
-                                               length=length, keywords=keywords, num_beams=1)
+                                            save_results=False,
+                                            length=length, keywords=keywords)
         elif filename[-4:] == ".pdf":
             results = ctrlsum_pipeline.predict(filename,
-                                               save_results=False, length=length, keywords=keywords, num_beams=1)
+                                            save_results=False, length=length, keywords=keywords)
         else:
             return "File Format Error !", None, filename
 
@@ -162,4 +83,5 @@ def ctrlsum_for_file(input=None, length=None, keywords="", text="", url="") -> L
         return "".join(output), results["raw_text"], filename
 
 
-ctrlsum_str_example = "Language model pre-training has been shown to be effective for improving many natural language processing tasks ( Dai and Le , 2015 ; Peters et al. , 2018a ; Radford et al. , 2018 ; Howard and Ruder , 2018 ) . These include sentence-level tasks such as natural language inference ( Bowman et al. , 2015 ; Williams et al. , 2018 ) and paraphrasing ( Dolan and Brockett , 2005 ) , which aim to predict the relationships between sentences by analyzing them holistically , as well as token-level tasks such as named entity recognition and question answering , where models are required to produce fine-grained output at the token level ( Tjong Kim Sang and De Meulder , 2003 ; Rajpurkar et al. , 2016 ) . There are two existing strategies for applying pre-trained language representations to downstream tasks : feature-based and fine-tuning . The feature-based approach , such as ELMo ( Peters et al. , 2018a ) , uses task-specific architectures that include the pre-trained representations as additional features . The fine-tuning approach , such as the Generative Pre-trained Transformer ( OpenAI GPT ) ( Radford et al. , 2018 ) , introduces minimal task-specific parameters , and is trained on the downstream tasks by simply fine-tuning all pretrained parameters . The two approaches share the same objective function during pre-training , where they use unidirectional language models to learn general language representations . We argue that current techniques restrict the power of the pre-trained representations , especially for the fine-tuning approaches . The major limitation is that standard language models are unidirectional , and this limits the choice of architectures that can be used during pre-training . For example , in OpenAI GPT , the authors use a left-toright architecture , where every token can only attend to previous tokens in the self-attention layers of the Transformer ( Vaswani et al. , 2017 ) . Such restrictions are sub-optimal for sentence-level tasks , and could be very harmful when applying finetuning based approaches to token-level tasks such as question answering , where it is crucial to incorporate context from both directions . In this paper , we improve the fine-tuning based approaches by proposing BERT : Bidirectional Encoder Representations from Transformers . BERT alleviates the previously mentioned unidirectionality constraint by using a `` masked language model '' ( MLM ) pre-training objective , inspired by the Cloze task ( Taylor , 1953 ) . The masked language model randomly masks some of the tokens from the input , and the objective is to predict the original vocabulary id of the masked arXiv:1810.04805v2 [ cs.CL ] 24 May 2019 word based only on its context . Unlike left-toright language model pre-training , the MLM objective enables the representation to fuse the left and the right context , which allows us to pretrain a deep bidirectional Transformer . In addition to the masked language model , we also use a `` next sentence prediction '' task that jointly pretrains text-pair representations . The contributions of our paper are as follows : • We demonstrate the importance of bidirectional pre-training for language representations . Unlike Radford et al . ( 2018 ) , which uses unidirectional language models for pre-training , BERT uses masked language models to enable pretrained deep bidirectional representations . This is also in contrast to Peters et al . ( 2018a ) , which uses a shallow concatenation of independently trained left-to-right and right-to-left LMs . • We show that pre-trained representations reduce the need for many heavily-engineered taskspecific architectures . BERT is the first finetuning based representation model that achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks , outperforming many task-specific architectures . • BERT advances the state of the art for eleven NLP tasks . The code and pre-trained models are available at https : //github.com/ google-research/bert . "
+
+ctrlsum_str_example = "Language model pre-training has been shown to be effective for improving many natural language processing tasks ( Dai and Le , 2015 ; Peters et al. , 2018a ; Radford et al. , 2018 ; Howard and Ruder , 2018 ) . These include sentence-level tasks such as natural language inference ( Bowman et al. , 2015 ; Williams et al. , 2018 ) and paraphrasing ( Dolan and Brockett , 2005 ) , which aim to predict the relationships between sentences by analyzing them holistically , as well as token-level tasks such as named entity recognition and question answering , where models are required to produce fine-grained output at the token level ( Tjong Kim Sang and De Meulder , 2003 ; Rajpurkar et al. , 2016 ) . There are two existing strategies for applying pre-trained language representations to downstream tasks : feature-based and fine-tuning . The feature-based approach , such as ELMo ( Peters et al. , 2018a ) , uses task-specific architectures that include the pre-trained representations as additional features . The fine-tuning approach , such as the Generative Pre-trained Transformer ( OpenAI GPT ) ( Radford et al. , 2018 ) , introduces minimal task-specific parameters , and is trained on the downstream tasks by simply fine-tuning all pretrained parameters . The two approaches share the same objective function during pre-training , where they use unidirectional language models to learn general language representations . We argue that current techniques restrict the power of the pre-trained representations , especially for the fine-tuning approaches . The major limitation is that standard language models are unidirectional , and this limits the choice of architectures that can be used during pre-training . For example , in OpenAI GPT , the authors use a left-toright architecture , where every token can only attend to previous tokens in the self-attention layers of the Transformer ( Vaswani et al. , 2017 ) . Such restrictions are sub-optimal for sentence-level tasks , and could be very harmful when applying finetuning based approaches to token-level tasks such as question answering , where it is crucial to incorporate context from both directions . In this paper , we improve the fine-tuning based approaches by proposing BERT : Bidirectional Encoder Representations from Transformers . BERT alleviates the previously mentioned unidirectionality constraint by using a `` masked language model '' ( MLM ) pre-training objective , inspired by the Cloze task ( Taylor , 1953 ) . The masked language model randomly masks some of the tokens from the input , and the objective is to predict the original vocabulary id of the masked arXiv:1810.04805v2 [ cs.CL ] 24 May 2019 word based only on its context . Unlike left-toright language model pre-training , the MLM objective enables the representation to fuse the left and the right context , which allows us to pretrain a deep bidirectional Transformer . In addition to the masked language model , we also use a `` next sentence prediction '' task that jointly pretrains text-pair representations . The contributions of our paper are as follows : • We demonstrate the importance of bidirectional pre-training for language representations . Unlike Radford et al . ( 2018 ) , which uses unidirectional language models for pre-training , BERT uses masked language models to enable pretrained deep bidirectional representations . This is also in contrast to Peters et al . ( 2018a ) , which uses a shallow concatenation of independently trained left-to-right and right-to-left LMs . • We show that pre-trained representations reduce the need for many heavily-engineered taskspecific architectures . BERT is the first finetuning based representation model that achieves state-of-the-art performance on a large suite of sentence-level and token-level tasks , outperforming many task-specific architectures . • BERT advances the state of the art for eleven NLP tasks . The code and pre-trained models are available at https : //github.com/ google-research/bert . "

description.py

@@ -1,8 +1,4 @@
 # Reference string parsing Markdown
-rsp_title_md = '''
-## Reference String Parsing parses a citation string, extracting information such as the title, authors, and publication date.
-'''
-
 rsp_str_md = '''
 To **test on strings**, simply input one or more strings.
 '''
@@ -46,8 +42,6 @@ To **test on strings**, simply input a string.
 ctrlsum_file_md = '''
 This is the demo for **CocoSciSum**.
 
-## Controlled Summarization uses FLAN-T5 to generate user-customised summaries from your input file or URL link.
-
 To **test on a file**, the input can be:
 
 - A txt file which contains the content to be summarized.
@@ -58,9 +52,7 @@ To **test on a file**, the input can be:
 
 '''
 
-de_title_md = '''
-## Dataset Extraction detects dataset mentions from the input text.
-'''
+
 
 de_str_md = '''
 To **test on strings**, please input your sentences or paragraphs.

requirements.txt

@@ -1,5 +1,4 @@
 pip==23.2.1
 torch==1.12.0
-SciAssist==0.1.4
+SciAssist==0.0.41
 nltk~=3.7
-pytest