Multidocument summarization via information extraction

White, Michael; Korelsky, Tanya; Cardie, Claire; Ng, Vincent; Pierce, David R.; Wagstaff, Kiri L.

doi:10.3115/1072133.1072206

Cited by 46 publications

(30 citation statements)

References 8 publications

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“…The majority of systems participating in the past Document Understanding Conference (DUC 2002), a large scale summarization evaluation effort sponsored by the US government, are extraction based. Although systems based on information extraction (Radev and McKeown 1998, White et al 2001, McKeown et al 2002 and discourse analysis (Marcu 1999b, Strzalkowski et al 1999) also exist, we focus our study on the potential and limitations of sentence extraction systems with the hope that our results will further progress in most of the automatic text summarization systems and evaluation setup. The evaluation results of the single document summarization task in DUC 2001(DUC 2002, Paul & Liggett 2002 indicate that most systems are as good as the baseline lead-based system and that humans are significantly better, though not by much.…”

Section: Introductionmentioning

confidence: 99%

The potential and limitations of automatic sentence extraction for summarization

Lin

Hovy

2003

Proceedings of the HLT-NAACL 03 on Text Summarization Workshop -

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In this paper we present an empirical study of the potential and limitation of sentence extraction in text summarization. Our results show that the single document generic summarization task as defined in DUC 2001 needs to be carefully refocused as reflected in the low inter-human agreement at 100-word 1 (0.40 score) and high upper bound at full text 2 (0.88) summaries. For 100-word summaries, the performance upper bound, 0.65, achieved oracle extracts 3 . Such oracle extracts show the promise of sentence extraction algorithms; however, we first need to raise inter-human agreement to be able to achieve this performance level. We show that compression is a promising direction and that the compression ratio of summaries affects average human and system performance.

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Section: Introductionmentioning

confidence: 99%

The potential and limitations of automatic sentence extraction for summarization

Lin

Hovy

2003

Proceedings of the HLT-NAACL 03 on Text Summarization Workshop -

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“…Similar to the SUMMONS system is a system called RIPTIDES (White et al, 2001). It incorporates information extraction to support summarization.…”

Section: News Summarizationmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

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“…Additional work on summarization [4,22,27,13] attempt on incorporating user query interests. However, they rely on naive heuristics of counting specific terms and defining manually extraction rules.…”

Section: Related Workmentioning

confidence: 99%

Summaries on the Fly: Query-Based Extraction of Structured Knowledge from Web Documents

Fetahu

Nunes

Dietze

2013

Lecture Notes in Computer Science

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Abstract. A large part of Web resources consists of unstructured textual content. Processing and retrieving relevant content for a particular information need is challenging for both machines and humans. While information retrieval techniques provide methods for detecting suitable resources for a particular query, information extraction techniques enable the extraction of structured data and text summarization allows the detection of important sentences. However, these techniques usually do not consider particular user interests and information needs. In this paper, we present a novel method to automatically generate structured summaries from user queries that uses POS patterns to identify relevant statements and entities in a certain context. Finally, we evaluate our work using the publicly available New York Times corpus, which shows the applicability of our method and the advantages over previous works.

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Multidocument summarization via information extraction

Abstract: We present and evaluate the initial version of RIPTIDES, a system that combines information extraction, extraction-based summarization, and natural language generation to support userdirected multidocument summarization.

Cited by 46 publications

References 8 publications

The potential and limitations of automatic sentence extraction for summarization

The potential and limitations of automatic sentence extraction for summarization

A Review on Automatic Text Summarization Approaches

Summaries on the Fly: Query-Based Extraction of Structured Knowledge from Web Documents

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