Improving LSA-based summarization with anaphora resolution

Shinar, J.; Kabadjov, Mijail; Poesio, Massimo; Sanchez-Graillet, Olivia

doi:10.3115/1220575.1220576

Cited by 21 publications

(17 citation statements)

References 14 publications

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“…Experiments on newswire texts show similar results [1] and lead to the conclusion that it may be possible to improve the results of the automatic summariser by using a coreference resolver instead of just a pronoun resolver. This conclusion is also supported by the research presented in [16] where the results on an LSA-based summariser are improved when a coreference resolver is used. In his thesis [17] and similar to our previous findings [1], Kabadjov establishes that the employment of his anaphora resolution system GUITAR to summarisation through substitution leads to limited (statistically insignificant) improvement.…”

Section: Automatic Summarisationsupporting

confidence: 65%

Coreference Resolution: To What Extent Does It Help NLP Applications?

Mitkov

Evans

Orăsan

et al. 2012

Text, Speech and Dialogue

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This paper describes a study of the impact of coreference resolution on NLP applications. Further to our previous study [1], in which we investigated whether anaphora resolution could be beneficial to NLP applications, we now seek to establish whether a different, but related task -that of coreference resolution, could improve the performance of three NLP applications: text summarisation, recognising textual entailment and text classification. The study discusses experiments in which the aforementioned applications were implemented in two versions, one in which the BART coreference resolution system was integrated and one in which it was not, and then tested in processing input text. The paper discusses the results obtained.

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Section: Automatic Summarisationsupporting

confidence: 65%

Coreference Resolution: To What Extent Does It Help NLP Applications?

Mitkov

Evans

Orăsan

et al. 2012

Text, Speech and Dialogue

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“…Bergler et al (2003) used the length of the noun phrase coreference chain to select the most important entities to form a 10-word summary. Steinberger et al (2005) showed that an LSA-based method using anaphora resolution helps summarization, but simply substituting with pronouns hurts the performance. Stoyanov and Cardie (2006) used coreference resolution to group the opinions from each source for opinion summarization.…”

Section: Related Workmentioning

confidence: 99%

Opinion summarization on spontaneous conversations

Wang

Liu

2015

Computer Speech & Language

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In this study we explore opinion summarization on spontaneous conversations using unsupervised and supervised approaches. We annotate a phone conversation corpus with reference extractive and abstractive summaries for a speaker's opinion on a given topic. We investigate two methods: the first is an unsupervised graph-based method, which incorporates topic and sentiment information, as well as sentence-to-sentence relations extracted based on dialogue structure; the second is a supervised method that casts the summarization problem as a classification problem. Furthermore, we investigate the use of pronoun resolution in this summarization task. We develop various features based on pronoun coreference and incorporate them in the supervised opinion summarization system. Our experimental results show that both the graph-based method and the supervised method outperform the baseline approach, and the pronoun related features can help to generate better summaries.

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“…Other variations of LSA based summarization approaches have later been introduced (Steinberger and Jezek, 2004;Steinberger et al, 2005;Lee et al, 2009;Ozsoy et al, 2010). Summarization approaches based on topic modeling and Bayesian models have also been explored (Vanderwende et al, 2007;Haghighi and Vanderwende, 2009; Celikyilmaz Citing article: ...…”

Section: Related Workmentioning

confidence: 99%

Scientific Article Summarization Using Citation-Context and Article's Discourse Structure

Cohan¹,

Goharian²

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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We propose a summarization approach for scientific articles which takes advantage of citation-context and the document discourse model. While citations have been previously used in generating scientific summaries, they lack the related context from the referenced article and therefore do not accurately reflect the article's content. Our method overcomes the problem of inconsistency between the citation summary and the article's content by providing context for each citation. We also leverage the inherent scientific article's discourse for producing better summaries. We show that our proposed method effectively improves over existing summarization approaches (greater than 30% improvement over the best performing baseline) in terms of ROUGE scores on TAC2014 scientific summarization dataset. While the dataset we use for evaluation is in the biomedical domain, most of our approaches are general and therefore adaptable to other domains.

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Improving LSA-based summarization with anaphora resolution

Cited by 21 publications

References 14 publications

Coreference Resolution: To What Extent Does It Help NLP Applications?

Coreference Resolution: To What Extent Does It Help NLP Applications?

Opinion summarization on spontaneous conversations

Scientific Article Summarization Using Citation-Context and Article's Discourse Structure

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