| { | |
| "paper_id": "O08-4001", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T08:02:36.113820Z" | |
| }, | |
| "title": "Multiple Document Summarization Using Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "authors": [ | |
| { | |
| "first": "Om", | |
| "middle": [], | |
| "last": "Vikas", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Akhil", | |
| "middle": [ | |
| "K" | |
| ], | |
| "last": "Meshram", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Girraj", | |
| "middle": [], | |
| "last": "Meena", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Amit", | |
| "middle": [], | |
| "last": "Gupta", | |
| "suffix": "", | |
| "affiliation": {}, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Text Summarization is very effective in relevant assessment tasks. The Multiple Document Summarizer presents a novel approach to select sentences from documents according to several heuristic features. Summaries are generated modeling the set of documents as Semantic Vector Space Model (SVSM) and applying Principal Component Analysis (PCA) to extract topic features. Pure Statistical VSM assumes terms to be independent of each other and may result in inconsistent results. Vector space is enhanced semantically by modifying the weight of the word vector governed by Appearance and Disappearance (Action class) words. The knowledge base for Action words is maintained by classifying the words as Appearance or Disappearance with the help of Wordnet. The weights of the action words are modified in accordance with the Object list prepared by the collection of nouns corresponding to the action words. Summary thus generated provides more informative content as semantics of natural language has been taken into consideration.", | |
| "pdf_parse": { | |
| "paper_id": "O08-4001", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "Text Summarization is very effective in relevant assessment tasks. The Multiple Document Summarizer presents a novel approach to select sentences from documents according to several heuristic features. Summaries are generated modeling the set of documents as Semantic Vector Space Model (SVSM) and applying Principal Component Analysis (PCA) to extract topic features. Pure Statistical VSM assumes terms to be independent of each other and may result in inconsistent results. Vector space is enhanced semantically by modifying the weight of the word vector governed by Appearance and Disappearance (Action class) words. The knowledge base for Action words is maintained by classifying the words as Appearance or Disappearance with the help of Wordnet. The weights of the action words are modified in accordance with the Object list prepared by the collection of nouns corresponding to the action words. Summary thus generated provides more informative content as semantics of natural language has been taken into consideration.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "With the advent of the information revolution, electronic documents are becoming a principal media of business and academic information. The Internet is being populated with hundreds of thousands of electronic documents each day. In order to fully utilize these on-line documents effectively, it is crucial to be able to extract the main idea of these documents. Having a Text Summarization system would thus be immensely useful in serving this need. Multiple Document Summarization System aids to provide the summary of a document set that \uff0a Indian Institute of Information Technology and Management, Gwalior, India-474010 E-mail: {omvikas, akhil, girrajmeena, amitgupta}@iiitm.ac.in contains documents which belong to same topic. It can also be used to generate the summary of a single document.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "In the present work, we propose a method of text summarization that uses semantics of data in order to form efficient and relevant summary. Summary is generated by constructing Statistical Vector Space Model (3.1) and then modifying it using the concept of Action words to form Semantic Vector Space Model (3.2). Action Words are identified using the Action Word Classifier which makes use of Wordnet [Kedar et al.] in order to analyze the semantics of word.", | |
| "cite_spans": [ | |
| { | |
| "start": 401, | |
| "end": 415, | |
| "text": "[Kedar et al.]", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Principal Component Analysis (3.3) is then applied on SVSM to reduce the dimension of multidimensional data sets. Singular Value Decomposition (SVD) is carried out on SVSM as a part of PCA to yield singular values and eigen vectors. Backprojection is then performed to project the documents onto the eigen space yielding projected values of documents which are henceforth compared with the singular values to yield the most relevant document/topic. Sentence Extraction (3.4) from multiple document sets has been assigned weight on the basis of keywords obtained from the most important document/topic. Sentences with higher weight are taken to form a summary.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "Various multiple document summarization systems already exist. This document summarizer is based on Kupeic 95 [Kupeic et al. 1995] which is a method of training a Bayesian classifier to recognize sentences that should belong in a summary. The classifier estimates the probability that a sentence belongs in a summary given a vector of features that are computed over the sentence. It identifies a set of features that correspond to the absence/presence of certain words or phrases and avoids the problem of having to analyze sentence structure. Their work focused on analyzing a single document at a time. Since then, there has been lot of work on the related problem of Multiple-document Summarization [Regina et al. 1999; Radev et al. 1998 ], where a system summarizes multiple documents on the same topic. For example, a system might summarize multiple news accounts of the recent massacre in Nepal; into a single document. Our hypothesis is that the similarities and differences between documents of the same type (e.g. bios of CS professors, earnings releases, etc.) provide information about the features that make a summary informative. The intuition is that the 'information content' of a document can be measured by the relationship between the document and a corpus of related documents. To be an informative summary, an abstract has to capture as much of the 'information content' as possible. To gain a handle on the problem of capturing the relationship between a document and a corpus, we examined several papers on Multiple-Document Summarization [Regina et al. 1999; Radev et al. 1998 Radev et al. , 2000 Radev et al. , 2004 Otterbacher et al. 2002] . However, we found most of their approaches were not applicable to Multiple Document Summarization Using 143 Principal Component Analysis Incorporating Semantic Vector Space Model our problem since they are mostly trying to match sentences of the same meaning to align multiple documents. The MEAD summarizer [Radev et al. 2000 [Radev et al. , 2001 , which was developed at the University of Michigan and at the Johns Hopkins University 2001 Summer Workshop on Automatic Summarization, produces summaries of one or more source articles (or a 'cluster' of topically related articles).", | |
| "cite_spans": [ | |
| { | |
| "start": 110, | |
| "end": 130, | |
| "text": "[Kupeic et al. 1995]", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 671, | |
| "end": 723, | |
| "text": "Multiple-document Summarization [Regina et al. 1999;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 724, | |
| "end": 741, | |
| "text": "Radev et al. 1998", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 1562, | |
| "end": 1582, | |
| "text": "[Regina et al. 1999;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 1583, | |
| "end": 1600, | |
| "text": "Radev et al. 1998", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 1601, | |
| "end": 1620, | |
| "text": "Radev et al. , 2000", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 1621, | |
| "end": 1640, | |
| "text": "Radev et al. , 2004", | |
| "ref_id": "BIBREF16" | |
| }, | |
| { | |
| "start": 1641, | |
| "end": 1665, | |
| "text": "Otterbacher et al. 2002]", | |
| "ref_id": "BIBREF9" | |
| }, | |
| { | |
| "start": 1976, | |
| "end": 1994, | |
| "text": "[Radev et al. 2000", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 1995, | |
| "end": 2015, | |
| "text": "[Radev et al. , 2001", | |
| "ref_id": "BIBREF15" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Related Work", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "Our Summarizer works on the documents belonging to same topic. It is strongly motivated by the analogy between this problem and the problem of face identification, where a system learns features for facial identification by applying PCA to find the characteristic eigenfaces [Turk et al. 1991; Pentland et al. 1994; Moon et al. 2001] .", | |
| "cite_spans": [ | |
| { | |
| "start": 275, | |
| "end": 293, | |
| "text": "[Turk et al. 1991;", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 294, | |
| "end": 315, | |
| "text": "Pentland et al. 1994;", | |
| "ref_id": "BIBREF10" | |
| }, | |
| { | |
| "start": 316, | |
| "end": 333, | |
| "text": "Moon et al. 2001]", | |
| "ref_id": "BIBREF8" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Related Work", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "Any set of documents dealing with the same subject is decomposed using Vector Space model. The important keywords can be extracted from the Vector Space Model using a threshold. Such keywords are called thematic keywords which are based on statistics. Important sentences can be extracted and a summary can be made using thematic keywords. We propose a new methodology for multiple document summarization by enhancing the VSM using semantics and identifying topic features based keywords to make the multiple document summary. The approach is:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "New Methodology", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "1. Statistical VSM construction from Multiple Document Set.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "New Methodology", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "Wordnet.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM generation using the concept of Contextual Action Words using", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "3. Application of PCA on Semantic VSM to reduce the dimension of the multidimensional data set yielding the most important Keywords.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM generation using the concept of Contextual Action Words using", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "4. Score Sentences based on several features such as sentence length cut-off feature, position feature, keyword weight, etc.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM generation using the concept of Contextual Action Words using", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "5. Generation of Summary extracting the sentences with high Score.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM generation using the concept of Contextual Action Words using", | |
| "sec_num": "2." | |
| }, | |
| { | |
| "text": "The Multiple Document Summarizer models the set of documents related to the same topic as the Statistical Vector Space Model based on several heuristics. The simplest way to transform a document into a vector is to define each unique word as a feature. The weight of a feature being decided based on the contribution of various parameters such as Cue-phrase Keywords, topic keywords and term frequency in document. The weight of the feature is being termed as Feature Combination.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Statistical VSM Construction", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "The vector representations of the documents; collectively define an n-dimensional vector space (where each document is an nx1 vector). The m document vectors taken as the columns of an nxm matrix D, define a linear transformation into the vector space.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Statistical VSM Construction", | |
| "sec_num": "3.1" | |
| }, | |
| { | |
| "text": "The existing vector space model is statistical in nature. This vector space is input to a number of tools and processes like a summarizer and information retrieval system. PCA/SVD Technique has been applied earlier for Summarization based on statistical vector space [Gong et al. 2001] . Some times this statistically generated model is unable to define the context.", | |
| "cite_spans": [ | |
| { | |
| "start": 267, | |
| "end": 285, | |
| "text": "[Gong et al. 2001]", | |
| "ref_id": "BIBREF4" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM Construction", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "Keywords identified by a statistical model can be non-contextual in nature. Therefore, an effort is to be made in the direction of identification of contextual keywords and modification of existing model so that it can be more helpful and contextual for various applications like text summarization and text retrieval.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM Construction", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "To identify the contextual keywords, we try to exploit human psychology. In any article, we identify that those words are important which either give a sense of either appearance or disappearance of any object/event. Thus, after we have the pure statistical vector space we need to enhance the vector space semantically by modifying the weights of the word vector by identifying the Appearance and Disappearance (ACTION class) words. To do so, we need to have a knowledgebase (KB) with some seed wordlist which belongs to appearance or disappearance. Following, are the steps involved in the semantic vector space model. 1. Get the tf matrix, T from existing document D.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM Construction", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "2. Identify the set of action words, A from the given tf matrix, T, (number of action words =n).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Semantic VSM Construction", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "A i ; A i \u03b5 A, 0<i<n.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Find the associated object list O i for action word", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "4. Find contextual objects Co from Object list O1,O2,\u2026.,On.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Find the associated object list O i for action word", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "5. Modify weight of contextual objects in T to form semantic vector space S{T}.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Find the associated object list O i for action word", | |
| "sec_num": "3." | |
| }, | |
| { | |
| "text": "Action words are the backbone of the semantic vector space model.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Identification of Action words", | |
| "sec_num": "3.2.1" | |
| }, | |
| { | |
| "text": "Definition: Action words are verbs that are used to strengthen the way experiences are presented whether it is expressing positive or negative experience.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Identification of Action words", | |
| "sec_num": "3.2.1" | |
| }, | |
| { | |
| "text": "With the help of Wordnet, the terms from the tf (term frequency) matrix which belong to the ACTION class can be easily classified. The algorithm uses a seed word list to identify the action words.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Identification of Action words", | |
| "sec_num": "3.2.1" | |
| }, | |
| { | |
| "text": "Whenever a term from the tf matrix is fetched, it is matched against seed word list. If it is matched, then the fetched term is action word; otherwise, synonyms of the fetched terms are matched.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multiple Document Summarization Using 145 Principal Component Analysis Incorporating Semantic Vector Space Model Definition: Seed Word List is the collection of action words. (Appendix A)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Given Input: T = {t1,t2,\u2026..,tn}. List type: A = { }, integer type: depth Do: for every t \u03b5 T depth = 0; match(t,seedwrdlst) if found then A = A U t. else, not found if(depth == 0) match(extractsynonym (t),seedwordlist) depth = 1 else continue endif endif endfor Output : A = {t1,t2,\u2026.tm}.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Multiple Document Summarization Using 145 Principal Component Analysis Incorporating Semantic Vector Space Model Definition: Seed Word List is the collection of action words. (Appendix A)", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "To decide whether the word belongs to action list or not, we have to build a seed wordlist and compare them with standard meaning. For example, let 'devastation' be the word to be decided as action or not. After searching in WordNet, the following meanings were obtained:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Figure 1. ACTION Word Classifier", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "\u2022 desolation (an event that results in total destruction) \u2022 destruction, (the termination of something by causing so much damage to it that it cannot be repaired or no longer exists)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Figure 1. ACTION Word Classifier", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "From the first and last meaning, it clearly lies in the phenomenon of appear/disappear so it will be appended into the seed list along with its Synset.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Figure 1. ACTION Word Classifier", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Merely acquiring the ACTION words doesn't provide the semantic to the vector space. We have to find whether these words are really important. The importance of the word can be estimated by the application of the word in the article. Objects corresponding to the Action Words and their weight in Statistical VSM have to be identified in order to determine the extent of relevancy of Action Words. The Objects are the Nouns or Adjectives for the Action. The nearest Noun for Verb is identified using POS Tagger and termed as Object of Action. Only those sentences are to be chosen which contain action words. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Finding the Objects of the Action", | |
| "sec_num": "3.2.2" | |
| }, | |
| { | |
| "text": "Contextual words are being defined as those action words which are applied to the important object. The Weight of Action Word is being taken as the maximum weight amongst all the objects corresponding to the given Action Word. The weight obtained is added to the weight of the corresponding Action Words in Statistical VSM yielding Semantic Vector Space Model for the given set of Documents.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Classification of Contextual Words", | |
| "sec_num": "3.2.3" | |
| }, | |
| { | |
| "text": "If we take an example of a single document:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Classification of Contextual Words", | |
| "sec_num": "3.2.3" | |
| }, | |
| { | |
| "text": "Today broke fire in Delhi. Mass Destruction of material happens due to this fire. Many suffered from the broken glass in the road. The authority arrives here soon. Till now there is report of any casualties in these fire except from few injures. Thanks for the local communities for help.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Classification of Contextual Words", | |
| "sec_num": "3.2.3" | |
| }, | |
| { | |
| "text": "the Vector Space generated on the basis of term frequency feature is The Action Word List obtained corresponding to the above example is: broke, destruction, and arrives. Now, the Action-Object list is prepared by identifying the Object words in which the ACTION words are acted. The Statistical VSM is now modified and the Semantic VSM is being generated as follows ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Classification of Contextual Words", | |
| "sec_num": "3.2.3" | |
| }, | |
| { | |
| "text": "Principal Component Analysis (PCA) [Michael et al. 2003 ] is used to reduce the multidimensional datasets to lower dimensions for analysis. Singular Value Decomposition (SVD) [Michael et al. 2003 ] is carried out on Semantic VSM to find the principal components of Vector Space. The singular value decomposition (SVD) of matrix A mxn is the factorization A=U\u2211V T , where U and V are orthogonal, and \u2211= diag (\u03c3 1,\u2026, \u03c3 r ), r= min (m,n), with \u03c3 1 \u2265 \u03c3 2 \u2265 \u2026\u2026\u2265 \u03c3 r \u22650 . The columns of V are the 'hidden' dimensions that we are looking for. The diagonal of \u2211 are the singular values which are the weights for the new set of basis vectors. \u2211 is symmetric, its singular values are its eigen values and its basis vectors are the eigen vectors.", | |
| "cite_spans": [ | |
| { | |
| "start": 35, | |
| "end": 55, | |
| "text": "[Michael et al. 2003", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 175, | |
| "end": 195, | |
| "text": "[Michael et al. 2003", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Principal Component Analysis", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Given an eigen vector e, we can find the corresponding dimension in document space.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Principal Component Analysis", | |
| "sec_num": "3.3" | |
| }, | |
| { | |
| "text": "Multiple Document Summarization Using", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "d= D.e", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "After determining out the dimension of eigen vector in document space, backprojection of d * is carried out. Commonly, composing a vector in terms of the principal components is called backprojection. Since our principal components or eigen documents are all orthogonal vectors, this is easy to accomplish. Let E be the matrix formed from the eigen documents then vector p is the document projected onto the eigenspace.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "sec_num": "149" | |
| }, | |
| { | |
| "text": "p = E T d", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "sec_num": "149" | |
| }, | |
| { | |
| "text": "Relevance of the topic/document is calculated by dividing projected component by the corresponding Singular Value. Metrics thus obtained is arranged in decreasing order excluding out the negative metrics. Main topic/Document is the one with highest metric value.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "sec_num": "149" | |
| }, | |
| { | |
| "text": "After selecting the main topic, we now need the topic keywords. We simply take the eigen document vector corresponding to main document and select the words with high weight. These are the set of Keywords which are of high relevance in summary.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "sec_num": "149" | |
| }, | |
| { | |
| "text": "To identify sentences that should belong to summary, several features have been taken into consideration.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sentence Extraction", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "\u2022 Sentence-Length Cut off Feature -If the sentence length is greater than 4 words, only then it is taken into consideration. \u2022 Position Feature -Sentences have been given some weight based on their position in the paragraph whether it is in initial, middle or final.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sentence Extraction", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "\u2022 Keywords -Sentence weight also depends not only on the number of keywords present in it but also the weight of each keyword.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sentence Extraction", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "\u2022 Upper Case Feature -Sentences containing upper case words have been given additional weight as it is probable that they may contain proper nouns.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sentence Extraction", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "Sentences with higher weight are taken as the relevant sentences for the summary and arranged in the order they appear in the document yielding the required summary. The rearrangement becomes a challenge in the case of multiple documents. In that case, sentences are kept at the position at which they appear in original document (initial/middle/final). This rearrangement technique provides fair results.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Sentence Extraction", | |
| "sec_num": "3.4" | |
| }, | |
| { | |
| "text": "The Multiple Document Summarization System is implemented in Java using JAMA (Java Matrix Package) and WVTool (Word Vector Tool) packages. JAMA is used to perform all the matrix operations as computing SVD, eigen vector, Backprojection, etc. WVTool is used to generate the Statistical Vector Space Model taking input as the Multiple Document Set or a Single document based on user requirement.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Implementation", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "The present section will focus on the accuracy of the proposed summarization method. The accuracy of the method was examined on both single as well as multiple document summaries:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Evaluation of Summarizer", | |
| "sec_num": "5." | |
| }, | |
| { | |
| "text": "Text belonging to different areas was taken. Summaries to the same texts were made by sentence extractions by different people. Based on the set of the summaries, we ranked sentences of the texts.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Single Document summary", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "We then carried out the summarization process using our algorithm, the Auto Summarizer in MS Word, and the Gnome Summarizer and compared their agreement on the extracted sentences with the human sentence extractions.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Single Document summary", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "The results are given in the following table. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Single Document summary", | |
| "sec_num": "5.1" | |
| }, | |
| { | |
| "text": "On an average, we get an average accuracy of 61.85% and improvement of 39.17% with respect to MS Word Summarizer.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "44.44% 47%", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The set of Documents belonging to \"Introduction to Web crawler\" were taken and then summary was generated using the proposed algorithm, and it was observed that the summary thus generated was in coherence with most of the documents. The input documents set consisting of documents related to the topic for summarization has been shown in Table 7 . The cause of the low contribution of Doc4 to the summary generated was observed to be sentences with fewer keywords in them with respect to sentences from other documents, resulting in a low score of sentence.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 338, | |
| "end": 345, | |
| "text": "Table 7", | |
| "ref_id": "TABREF4" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Multiple Documents Summary", | |
| "sec_num": "5.2" | |
| }, | |
| { | |
| "text": "As seen from the results, the proposed method works better for various domains and, by using Semantic VSM instead of Statistical VSM; the summary obtained has become more informational and meaningful. Moreover, this method can be used to generate single as well as multiple document summaries.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "6." | |
| }, | |
| { | |
| "text": "The following areas in Multiple Document Summarization System require improvement: 1 Documents for Summary % age in sum m ary Doc1 Doc2 Doc3 Doc4 1. Rearrangement of Extracted Sentences in the case of Multiple Documents Summarization to form an effective summary.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "6." | |
| }, | |
| { | |
| "text": "2. Enhance Flexibility of the system to generate a summary of multiple documents not necessarily belonging to the same topic.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "6." | |
| }, | |
| { | |
| "text": "3. Develop better methodology to incorporate the ACTION word score into Statistical VSM.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "6." | |
| }, | |
| { | |
| "text": "4. Evaluation of the system on large data samples. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "6." | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "Information fusion in the context of multi-document summarization", | |
| "authors": [ | |
| { | |
| "first": "R", | |
| "middle": [], | |
| "last": "Barzilay", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2003, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Barzilay, R., \"Information fusion in the context of multi-document summarization,\" Phd. Thesis, Columbia University, 2003.", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Generic Text Summarization using WordNet", | |
| "authors": [ | |
| { | |
| "first": "K", | |
| "middle": [], | |
| "last": "Bellare", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Sarma", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Sarma", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "N", | |
| "middle": [], | |
| "last": "Loiwal", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "V", | |
| "middle": [], | |
| "last": "Mehta", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "G", | |
| "middle": [], | |
| "last": "Ramakrishnan", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "P", | |
| "middle": [], | |
| "last": "Bhattacharya", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Bellare, K., A. Das Sarma, A. Das Sarma, N. Loiwal, V. Mehta, G. Ramakrishnan, and P. Bhattacharya, Generic Text Summarization using WordNet, http://i.stanford.edu/ ~anishds/publications/lrec04/lrec04.ps.", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "Using linear algebra for intelligent information-retrieval", | |
| "authors": [ | |
| { | |
| "first": "M", | |
| "middle": [ | |
| "W" | |
| ], | |
| "last": "Berry", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "S", | |
| "middle": [ | |
| "T" | |
| ], | |
| "last": "Dumais", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "G", | |
| "middle": [ | |
| "W" | |
| ], | |
| "last": "Obrien", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1995, | |
| "venue": "Siam Review", | |
| "volume": "37", | |
| "issue": "", | |
| "pages": "573--95", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Berry, M. W., S. T. Dumais, and G. W. Obrien, \"Using linear algebra for intelligent information-retrieval,\" Siam Review, 37, 1995, pp. 573-95.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis", | |
| "authors": [ | |
| { | |
| "first": "Y", | |
| "middle": [], | |
| "last": "Gong", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "X", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2001, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "19--25", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Gong, Y., and X. Liu, \"Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis,\" SIGIR 2001, pp. 19-25.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "A parallel algorithm for computing the singular-value decomposition of a matrix", | |
| "authors": [ | |
| { | |
| "first": "E", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Jessup", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "D", | |
| "middle": [ | |
| "C" | |
| ], | |
| "last": "Sorensen", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1994, | |
| "venue": "Siam Journal on Matrix Analysis and Applications", | |
| "volume": "15", | |
| "issue": "", | |
| "pages": "530--548", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Jessup, E. R., and D. C. Sorensen, \"A parallel algorithm for computing the singular-value decomposition of a matrix,\" Siam Journal on Matrix Analysis and Applications, 15, 1994, pp. 530-548.", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "Principal Component Analysis", | |
| "authors": [ | |
| { | |
| "first": "I", | |
| "middle": [ | |
| "T" | |
| ], | |
| "last": "Jolliffe", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1986, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Jolliffe, I. T., Principal Component Analysis, New York: Springer, 1986.", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "A Trainable Document Summarizer", | |
| "authors": [ | |
| { | |
| "first": "J", | |
| "middle": [], | |
| "last": "Kupiec", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "J", | |
| "middle": [], | |
| "last": "Pedersen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "F", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1995, | |
| "venue": "Proceedings of the 18th ACM-SIGIR Conference", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "68--73", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Kupiec, J., J. Pedersen, and F. Chen, \"A Trainable Document Summarizer,\" In Proceedings of the 18th ACM-SIGIR Conference, 1995, pp. 68-73.", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "Computational and Performance aspects of PCA-based Face Recognition Algorithms", | |
| "authors": [ | |
| { | |
| "first": "H", | |
| "middle": [], | |
| "last": "Moon", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "P", | |
| "middle": [ | |
| "J" | |
| ], | |
| "last": "Phillips", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2001, | |
| "venue": "Perception", | |
| "volume": "30", | |
| "issue": "", | |
| "pages": "303--321", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Moon, H., and P. J. Phillips, \"Computational and Performance aspects of PCA-based Face Recognition Algorithms,\" Perception, 30, 2001, pp. 303-321.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "The Michigan Single and Multidocument Summarizer for DUC", | |
| "authors": [ | |
| { | |
| "first": "J", | |
| "middle": [ | |
| "C" | |
| ], | |
| "last": "Otterbacher", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [ | |
| "J" | |
| ], | |
| "last": "Winkel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "D", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Radev", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2002, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Otterbacher, J. C., A. J. Winkel, and D. R. Radev, The Michigan Single and Multidocument Summarizer for DUC 2002, http://www-nlpir.nist.gov/projects/duc/pubs/2002papers/umich_otter.pdf.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "View-Based and Modular Eigenspaces for Face Recognition", | |
| "authors": [ | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Pentland", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "B", | |
| "middle": [], | |
| "last": "Moghaddam", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "T", | |
| "middle": [], | |
| "last": "Starner", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1994, | |
| "venue": "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "84--91", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Pentland, A., B. Moghaddam, and T. Starner, \"View-Based and Modular Eigenspaces for Face Recognition,\" Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 21-23 June, 1994, Seattle, Washington, USA, pp. 84-91.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Generating natural language summaries from multiple on-line sources", | |
| "authors": [ | |
| { | |
| "first": "D", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Radev", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "K", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Mckeown", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1998, | |
| "venue": "Computational Linguistics", | |
| "volume": "24", | |
| "issue": "3", | |
| "pages": "469--500", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Radev, D. R., and K. R. McKeown, \"Generating natural language summaries from multiple on-line sources,\" Computational Linguistics, 24(3), 1998, pp. 469-500.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "Multiple Document Summarization Using 153", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Multiple Document Summarization Using 153", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "authors": [], | |
| "year": null, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Principal Component Analysis Incorporating Semantic Vector Space Model", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "Centroid-based summar-ization of multiple documents: sentence extraction, util-ity based evaluation, and user studies", | |
| "authors": [ | |
| { | |
| "first": "D", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Radev", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "H", | |
| "middle": [], | |
| "last": "Jing", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Budzikowska", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2000, | |
| "venue": "ANLP /NAACL Workshop on Summarization", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Radev, D. R., H. Jing, and M. Budzikowska, \"Centroid-based summar-ization of multiple documents: sentence extraction, util-ity based evaluation, and user studies,\" In ANLP /NAACL Workshop on Summarization, Seattle, WA, April 2000.", | |
| "links": null | |
| }, | |
| "BIBREF15": { | |
| "ref_id": "b15", | |
| "title": "Evaluation challenges in large-scale multidocument summarization: the MEAD project", | |
| "authors": [ | |
| { | |
| "first": "D", | |
| "middle": [], | |
| "last": "Radev", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "S", | |
| "middle": [], | |
| "last": "Teufel", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "H", | |
| "middle": [], | |
| "last": "Saggion", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "W", | |
| "middle": [], | |
| "last": "Lam", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "J", | |
| "middle": [], | |
| "last": "Blitzer", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Celebi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "H", | |
| "middle": [], | |
| "last": "Qi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "D", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "E", | |
| "middle": [], | |
| "last": "Drabek", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2001, | |
| "venue": "Johns Hopkins University CLSP Workshop Final Report", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Radev, D., S. Teufel, H. Saggion, W. Lam, J. Blitzer, A. Celebi, H. Qi, D. Liu, and E. Drabek, \"Evaluation challenges in large-scale multidocument summarization: the MEAD project,\" Johns Hopkins University CLSP Workshop Final Report, 2001.", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "Centroid-based summarization of multiple documents", | |
| "authors": [ | |
| { | |
| "first": "D", | |
| "middle": [ | |
| "R" | |
| ], | |
| "last": "Radev", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "H", | |
| "middle": [], | |
| "last": "Jing", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Sty", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "D", | |
| "middle": [], | |
| "last": "Tam", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Information Processing and Management", | |
| "volume": "40", | |
| "issue": "", | |
| "pages": "919--957", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Radev, D. R., H. Jing, M. Sty, and D. Tam, \"Centroid-based summarization of multiple documents,\" Information Processing and Management, 40, 2004, pp. 919-38.", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "The Smart Retrieval System", | |
| "authors": [ | |
| { | |
| "first": "G", | |
| "middle": [], | |
| "last": "Salton", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1971, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Salton, G., The Smart Retrieval System, Prentice Hall, Englewood Cliffs, N.J. 1971.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "Introduction to Linear Algebra", | |
| "authors": [ | |
| { | |
| "first": "G", | |
| "middle": [], | |
| "last": "Strang", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1998, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Strang, G., Introduction to Linear Algebra, Wellesley, MA: Wellesley Cambridge Press, 1998.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "Eigenfaces for Face Detection/Recognition", | |
| "authors": [ | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Turk", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Pentland", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1991, | |
| "venue": "Journal of Cognitive Neuroscience", | |
| "volume": "3", | |
| "issue": "1", | |
| "pages": "71--86", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Turk, M., and A. Pentland, \"Eigenfaces for Face Detection/Recognition,\" Journal of Cognitive Neuroscience, 3(1), 1991, pp. 71-86.", | |
| "links": null | |
| }, | |
| "BIBREF20": { | |
| "ref_id": "b20", | |
| "title": "Singular Value Decomposition and Principal Component Analysis", | |
| "authors": [ | |
| { | |
| "first": "M", | |
| "middle": [ | |
| "E" | |
| ], | |
| "last": "Wall", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [], | |
| "last": "Rechtsteinen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "L", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Rocha", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2003, | |
| "venue": "A Practical Approach to Microarray Data Analysis", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "91--109", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Wall, M. E., A. Rechtsteinen, and L. M. Rocha, \"Singular Value Decomposition and Principal Component Analysis,\" In A Practical Approach to Microarray Data Analysis (D.P. Berrar, W. Dubitzky, M. Granzow, eds.) Kluwer: Norwell, MA, 2003, pp. 91-109, LANL LA-UR-02-4001.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF0": { | |
| "num": null, | |
| "uris": null, | |
| "text": "ravaging, (plundering with excessive damage and destruction)", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF1": { | |
| "num": null, | |
| "uris": null, | |
| "text": "broke fire in Delhi. (Action is verb) Today/NN broke/VBD fire/NN in/IN Delhi/NNP Destruction of material happens due to this fire. Destruction//NN of/IN material/NN happens/VBZ due/JJ to/TO this/DT fire/NN ./. Many suffered from the broken glass in the road. (Action is Adjective) Many/JJ suffered/VBD from/IN the/DT broken/JJ glass/NN in/IN the/DT road/NN. /. The authority arrives here soon.The/DT authority/NN arrives/VBZ here/RB soon/RB. /.", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF2": { | |
| "num": null, | |
| "uris": null, | |
| "text": "has been given weight as per the contextual word obtained corresponding to it.", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF4": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Figure 2. Contribution percentage", | |
| "type_str": "figure" | |
| }, | |
| "FIGREF6": { | |
| "num": null, | |
| "uris": null, | |
| "text": "Schemed Screened Set goals Shaped Skilled Solicited Solved Specialized Spoke Stimulated Strategized Streamlined Strengthened Stressed Studied Substantiated Succeeded Summarized Synthesized", | |
| "type_str": "figure" | |
| }, | |
| "TABREF0": { | |
| "num": null, | |
| "text": "", | |
| "html": null, | |
| "content": "<table><tr><td>broke</td><td>fire, glass</td></tr><tr><td>arrives</td><td>Authority</td></tr><tr><td>destruction</td><td>material</td></tr></table>", | |
| "type_str": "table" | |
| }, | |
| "TABREF1": { | |
| "num": null, | |
| "text": "", | |
| "html": null, | |
| "content": "<table><tr><td>Broke</td><td>0.1889 Injuries</td><td>0.1889</td></tr><tr><td>Fire</td><td>0.5669 Thanks</td><td>0.1889</td></tr><tr><td>Delhi</td><td>0.1889 Local</td><td>0.1889</td></tr><tr><td>Mass</td><td>0.1889 communities</td><td>0.1889</td></tr><tr><td>Destruction</td><td>0.1889 Help</td><td>0.1889</td></tr><tr><td>Material</td><td>0.1889 Authority</td><td>0.1889</td></tr><tr><td>Happens</td><td>0.1889 Arrives</td><td>0.1889</td></tr><tr><td>Suffered</td><td>0.1889 Report</td><td>0.1889</td></tr><tr><td>Broken</td><td>0.1889 Casualties</td><td>0.1889</td></tr><tr><td>glass</td><td>0.1889 Road</td><td>0.1889</td></tr></table>", | |
| "type_str": "table" | |
| }, | |
| "TABREF2": { | |
| "num": null, | |
| "text": "Similarly, the model is extended for multiple documents. This Semantic Vector Space Model is used further to determine important Keywords and henceforth, the summary.", | |
| "html": null, | |
| "content": "<table><tr><td/><td>0.7558 Injuries</td><td>0.1889</td></tr><tr><td>Fire</td><td>0.5669 Thanks</td><td>0.1889</td></tr><tr><td>Delhi</td><td>0.1889 Local</td><td>0.1889</td></tr><tr><td>Mass</td><td>0.1889 communities</td><td>0.1889</td></tr><tr><td colspan=\"2\">Destruction 0.3778 Help</td><td>0.1889</td></tr><tr><td>Material</td><td>0.1889 Authority</td><td>0.1889</td></tr><tr><td>Happens</td><td>0.1889 Arrives</td><td>0.3778</td></tr><tr><td>Suffered</td><td>0.1889 Report</td><td>0.1889</td></tr><tr><td>Broken</td><td>0.1889 Casualties</td><td>0.1889</td></tr><tr><td>glass</td><td>0.1889 Road</td><td>0.1889</td></tr></table>", | |
| "type_str": "table" | |
| }, | |
| "TABREF3": { | |
| "num": null, | |
| "text": "", | |
| "html": null, | |
| "content": "<table><tr><td>Science (789 words)</td><td>60.0%</td><td>50.0%</td><td>70.0%</td></tr><tr><td>Geography (725 words)</td><td>55.56%</td><td>33.33%</td><td>22.5%</td></tr><tr><td>History (557 words)</td><td>70.0%</td><td>50.0%</td><td>48.5%</td></tr><tr><td>Average accuracy</td><td>61.85%</td><td/><td/></tr></table>", | |
| "type_str": "table" | |
| }, | |
| "TABREF4": { | |
| "num": null, | |
| "text": "", | |
| "html": null, | |
| "content": "<table><tr><td>Doc No.</td><td>Title of Doc</td><td>Doc Length</td></tr><tr><td>Doc1</td><td>Introduction to Crawler Architecture</td><td>1076 words</td></tr><tr><td>Doc2</td><td>Developing Web Search Engine</td><td>890 words</td></tr><tr><td>Doc3</td><td>Overview of Web Crawler</td><td>945 words</td></tr><tr><td>Doc4</td><td>Future of Search Engines</td><td>970 words</td></tr></table>", | |
| "type_str": "table" | |
| } | |
| } | |
| } | |
| } |