Regina Barzilay and Michael Elhadad. 1997. Using lexical chains for text summarization. In Proceedings of the ACL Intelligent Scalable Text Summarization Workshop, pages 86--90.  Home/Search Document Details and Download
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Logic Form Transformation for WordNet Glosses and its Applications: .. - Rus (2001) (Correct)
....proximity expressed by WordNet s links, and a poor algorithm for chaining. Over chaining the linking of two poorly related words might happen whenever two semantically distant words are close to each other in WordNet s graph. Over chaining often results in the merging of two chains. Barzilay [2] uses lexical chains in texts for summarization. Summarization proceeds in three steps: the original text is rst segmented, lexical chains are then constructed, strong chains are identi ed and signi cant sentences are extracted from text. This algorithm relies heavily on WordNet as a lexical ....
....stopped. Reason: extra argument mismatching. shoot:v#2(e100,x300,x2) shoot:v#2(e100,x400,x100) Backtrack . unify q[0] person:n#1(x1) with person:n#1(x400) Uni cation done. New Step 2 unify q[1] shoot:v#2(e1,x400,x2) with shoot:v#2(e100,x400,x100) Uni cation done. New Step 3 unify q[2]: billy the kid:n#1(x2) with billy the kid:n#1(x100) 89 Uni cation done. Question PROVEN question predicates person:n#1(x400) shoot:v#2(e100,x400,x100) billy the kid:n#1(x100) answer predicates nn:n#1(x100,x200,x300) outlaw:n#1(x200) william bonney:n#1(x300) billy the kid:n#1(x100) ....
Regina Barzilay and Michael Elhadad. Using lexical chains for text summarization. In In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97), Madrid, 1997. ACL.
An Efficient WordNet-Based Summarizer for Large Text - Documents Raymond Wong (Correct)
....needs in a more effective and efficient way. In this paper, a sentence based extraction summarization system which takes in a single document from any domain was developed to produce an indicative summary as output. The approach taken was initially similar to that taken by Barzilay and Elhadad [1]. It is based on the use of lexical chains, which can be computed without requiring deep text understanding while can still be able to determine the context of the text at the same time. However, different to Barzilay and Elhadad, except the semantic relatedness of words in the text was ....
....term repetition to construct a weighted graph with nodes being the paragraphs and the weighted arcs representing the similarity between them. Another class of this cohesion based system is the use of lexical chain, whose concept was first introduced by Morris and Hirst [31] Barzilay and Elhadad [1] explored the use of lexical chains and used WordNet as their knowledge base. They claimed that by using lexical chains, the readers will be able to get a better indication of the topic of the text compared to simply picking the most frequent words in the text (i.e. frequency based) In their ....
Regina Barzilay and Michael Elhadad. Using lexical chains for text summarization. In Proceedings of the ACL'97/EACL'07 Workshop on Intelligent Scalable Text Summarization, Madrid,
Tailoring Text Using Topic Words: Selection and Compression - Euler (2002) (1 citation) (Correct)
....work In text summarization, it is a standard approach to select sentences from the original text for the summary based on characteristic words [10] Such words and ways to find them are of special interest here. One approach is to use lexical knowledge from sources such as WORDNET [11] compare [2]. Another is to use statistical information gathered from a corpus [1] A well known measure that relies only on the given text collection is Tf Idf (used, for example, in [3, 4] which measures how characteristic a word is for its text. Considering domain specific summarization, words that are ....
R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In I. Mani and M. T. Maybury, editors, Advances in Automatic Text Summarization, pages 111--121. MIT Press, Cambridge, MA, 1999.
Model Selection in Summary Evaluation - Perez-Breva, Yoshimi (2002) (Correct)
....Step Representation Assumptions Algorithm Lexical Inference Word sequence Lexical Cohesion, Distance Greedy strategy with word Chains metric in wordnet [6] memory , lexical chain summa pruning. rization Feature se Sentences, lexi Important sentences are the Sentence scoring through [2] leerich cal chains highest connected entities lexical chains Compilation Sentences Independence between units Display selection in order of of content appearance Lead Inference None Feature se Sentences Important sentences are lo Pick the first n sentences Based lection cared at the ....
....by ideas in the original document will allow to increase the similarities between sentences for the headline based algorithm for sentence selection. Note that tracking the words coupled with each idea in the sequence will yield the thread of an idea, similar to the lexical chains described in [2]. Two options are preferred to train the Hidden Markov model: 1. Estimate a Hidden Markov Model from a set of documents parsed as sequences of known words. This may be achieved through the EM algorithm. 2. Learn both the structure of the Hidden Markov Model and its parameters. By either ....
R. Barzilay and M. Elhadad. Using lexical chains for text summarization, 1997.
Segmenting Broadcast News Streams using Lexical Chains - Stokes, Carthy, Smeaton (2002) (4 citations) (Correct)
.... text rather than vast quantities of semi relevant documents [1] Summarisation is another task that can be greatly improved by well segmented text since the aim of this task is to identify pertinent subtopics in a document and then generate a summary, which encapsulates all of these subtopics [2]. The main motivation of our research is to investigate whether our lexical chaining technique can be used to segment television news shows into distinct new stories. Lexical chaining is a linguistic technique that uses an auxiliary resource (in our case the WordNet online thesaurus [3] to ....
R. Barzilay, M. Elhadad, Using Lexical Chains for Text Summarization. In the proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97), ACL, Madrid, 1997.
The Columbia Multi-Document Summarizer for DUC 2002 - Kathleen Mckeown David (2002) (1 citation) Self-citation (Barzilay) (Correct)
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Regina Barzilay and Michael Elhadad. 1997. Using lexical chains for text summarization. In Proceedings of the ACL Workshop on Intelligent Scalable Text Summarization, pages 10--17, Madrid, Spain, August. Association for Computational Linguistics.
Columbia Multi-Document Summarization: Approach and.. - McKeown.. (2001) (4 citations) Self-citation (Barzilay) (Correct)
....intersection parameters for more verbose output and rank the different themes. This ranking is based on theme size, similarity score and significance. The first two of these scores are produced by the similarity component, and the significance score of the theme is computed using lexical chains [Barzilay and Elhadad 1997], as the sum of lexical chain scores of theme sentences computed from the text in which a sentence originally belongs. Lexical chains, sequences of semantically related words, are tightly connected to the lexical cohe sive structure of the text and have been shown to be useful for determining ....
Regina Barzilay and Michael Elhadad. Using Lexical Chains for Text Summarization. In Proceedings of the A CL Workshop on Intelligent Scalable Text Summarization, pages 10-17, Madrid, Spain, August 1997. Association for Computational Linguistics.
Using Readers to Identify Lexical Cohesive Structures in Texts - Beata Beigman Klebanov (2005) (Correct)
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Regina Barzilay and Michael Elhadad. 1997. Using lexical chains for text summarization. In Proceedings of the ACL Intelligent Scalable Text Summarization Workshop, pages 86--90.
Semantic Relatedness: - Computational Investigation Of (Correct)
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Regina Barzilay and Michael Elhadad. 1997. Using lexical chains for text summarization. In Proceedings of ACL Intelligent Scalable Text Summarization Workshop.
Using People and WordNet to Measure - Semantic Relatedness Beata (Correct)
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Regina Barzilay and Michael Elhadad. Using lexical chains for text summarization. In Proceedings of ACL Intelligent Scalable Text Summarization Workshop, 1997.
BioChain: Lexical Chaining Methods for Biomedical Text.. - Reeve, Han, Brooks (2006) (Correct)
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R. Barzilay and M. Elhadad, "Using Lexical Chains for Text Summarization," in Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97), ACL, 1997, pp. 10-18.
Improving Word Sense Disambiguation in Lexical Chaining - Galley, McKeown (2003) (4 citations) (Correct)
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R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In Proc. of the Intelligent Scalable Text Summarization Workshop (ISTS'97), ACL, 1997.
Adapting Lexical Chaining to Summarize Conversational Dialogues - Iryna Gurevych Eml (2005) (Correct)
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Regina Barzilay and Michael Elhadad. 1999. Using lexical chains for text summarization. In Inderjeet Mani and Mark T.
Summarizing Texts at Various Levels of Detail - Marie-Francine Moens Roxana (Correct)
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Barzilay, R. & Elhadad, M. (1999). Using lexical chains for text summarization. In I. Mani & M.T. Maybury (Eds.), Advances in Automatic Text Summarization (pp. 111-121). Cambridge, MA: MIT Press.
Applications of Lexical Cohesion in the Topic Detection and.. - Stokes (2004) (Correct)
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, Workshop on Intelligent Scalable Text Summarization, pp. 10-17, 1997.
SeLeCT: A Lexical Cohesion Based News Story Segmentation.. - Stokes, Carthy, Smeaton (2004) (2 citations) (Correct)
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R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In the Proceedings of the Intelligent Scalable Text Summarization Workshop, 1997.
Apprentissage Numrique pour le Rsum de Texte - Massih-Rza Amini Patrick (Correct)
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R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In Proceedings of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization, pages 10--17, 1997.
A Conceptual Model of Information Supply - Van Gils Proper (2003) (Correct)
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R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97), Madrid, Spain, 1997.
Apprentissage Numrique pour le Rsum de Texte - Massih-Rza Amini Patrick (Correct)
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R. Barzilay and M. Elhadad. Using lexical chains for text summarization. In Proceedings of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization, pages 10--17, 1997.
WordNet for Lexical Cohesion Analysis - Elke Teich And (2004) (Correct)
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Barzilay, R., Elhadad, M.: Using lexical chains for text summarization. In: Proceedings of ISTS 97, ACL, Madrid, Spain (1997).
First Story Detection using a Composite Document - Stokes (Correct)
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R. Barzilay, M. Elhadad, Using Lexical Chains for Text Summarization, In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97), ACL, Madrid, 1997.
A Comparison of Automatic Summarizers of Texts in.. - Rino, Pardo, Jr.. (Correct)
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Barzilay, R., Elhadad, M.: Using Lexical Chains for Text Summarization. In: Advances in Automatic Text Summarization. MIT Press (1999) 111--121
Coherent Keyphrase Extraction via Web Mining - Turney (2003) (2 citations) (Correct)
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Barzilay, R., and Elhadad, M. Using lexical chains for text summarization. In Proceedings of the ACL'97/EACL'97 Workshop on Intelligent Scalable Text Summarization, 10-17, 1997.
GistSumm: A Summarization Tool Based on a New Extractive Method - Pardo, Rino, Nunes (2003) (Correct)
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Barzilay, R. and Elhadad, M.: Using lexical chains for text summarization. In the Proceedings of the ACL-97/EACL97 Workshop on Intelligent Scalable Text Summarization. Madrid, Spain (1998).
Logic Forms for Wordnet Glosses - Rus (2002) (Correct)
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Barzilay, R., and Elhadad, M. Using lexical chains for text summarization. In In Proceedings of the Intelligent Scalable Text Summarization Workshop (ISTS'97) (Madrid, 1997), ACL.
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