A formal model for information selection in multi-sentence text extraction

Filatova, Elena; Hatzivassiloglou, Vasileios

doi:10.3115/1220355.1220412

Cited by 91 publications

(76 citation statements)

References 13 publications

(4 reference statements)

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“…That is, we can obtain a document comprising a complete answer on the basis of a request (i.e., re-use an answer to a previous request), or we can obtain individual sentences and then combine them to compose an answer, as is done in multi-document summarization [2]. The sentence-level granularity enables the re-use of a sentence for different responses, as well as the composition of partial responses.…”

Section: Ra2: Is There a Way To Disable The Nat Firewall On The Compamentioning

confidence: 99%

“…Because the sentences in each cluster originate from different response documents, the process of selecting them for a new response corresponds to multi-document summarization. In fact, our selection mechanism, described in more detail in [6], is based on a multi-document summarization formulation proposed by Filatova and Hatzivassiloglou [2].…”

Section: Predict Sentencesmentioning

confidence: 99%

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A Comparative Study of Information-Gathering Approaches for Answering Help-Desk Email Inquiries

Zukerman

Marom

2006

Lecture Notes in Computer Science

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Abstract. We present a comparative study of corpus-based methods for the automatic synthesis of email responses to help-desk requests. Our methods were developed by considering two operational dimensions: (1) information-gathering technique, and (2) granularity of the information. In particular, we investigate two techniques -retrieval and prediction -applied to information represented at two levels of granularity -sentence level and document level. We also developed a hybrid method that combines prediction with retrieval. Our results show that the different approaches are applicable in different situations, addressing a combined 72% of the requests with either complete or partial responses.

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Section: Ra2: Is There a Way To Disable The Nat Firewall On The Compamentioning

confidence: 99%

Section: Predict Sentencesmentioning

confidence: 99%

A Comparative Study of Information-Gathering Approaches for Answering Help-Desk Email Inquiries

Zukerman

Marom

2006

Lecture Notes in Computer Science

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“…They also formally evaluated the performance of this idea. More recently, Filatova and Hatzivassiloglou ( [14]) considered the contexts involving any pair of names as general 'events' and used them to improve extractive summarization. Vanderwende et al ([15]) explored an event-centric approach and generated summaries based on extracting and merging portions of logical forms.…”

Section: Related Workmentioning

confidence: 99%

Re-ranking Summaries Based on Cross-Document Information Extraction

Liu

Favre

et al. 2010

Information Retrieval Technology

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Abstract. This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorporate IE results into sentence ranking. Experiments have shown our integration methods can significantly improve a high-performing multi-document summarization system, according to the ROUGE-2 and ROUGE-SU4 metrics (7.38%% relative improvement on ROUGE-2 recall), and the generated summaries are preferred by human subjects (0.78 higher TAC Content score and 0.11 higher Readability/Fluency score).

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“…Filatova and Hatzivassiloglou [19] modeled extractive text summarization as a maximum coverage problem that aims at covering as many conceptual units as possible by selecting some sentences. McDonald [20] formalized text summarization as a knapsack problem and obtained the global solution and its approximate solutions.…”

Section: Related Workmentioning

confidence: 99%

Multi-Document Summarization Model Based on Integer Linear Programming

Alguliyev¹,

Alıguliyev²,

Hajirahimova³

2010

ICA

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This paper proposes an extractive generic text summarization model that generates summaries by selecting sentences according to their scores. Sentence scores are calculated using their extensive coverage of the main content of the text, and summaries are created by extracting the highest scored sentences from the original document. The model formalized as a multiobjective integer programming problem. An advantage of this model is that it can cover the main content of source (s) and provide less redundancy in the generated summaries. To extract sentences which form a summary with an extensive coverage of the main content of the text and less redundancy, have been used the similarity of sentences to the original document and the similarity between sentences. Performance evaluation is conducted by comparing summarization outputs with manual summaries of DUC2004 dataset. Experiments showed that the proposed approach outperforms the related methods.

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A formal model for information selection in multi-sentence text extraction

Cited by 91 publications

References 13 publications

A Comparative Study of Information-Gathering Approaches for Answering Help-Desk Email Inquiries

A Comparative Study of Information-Gathering Approaches for Answering Help-Desk Email Inquiries

Re-ranking Summaries Based on Cross-Document Information Extraction

Multi-Document Summarization Model Based on Integer Linear Programming

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