Mining discourse markers for Chinese textual summarization

Chan, Samuel W. K.; Lai, Tom B. Y.; Gao, W. J.; Tsou, Benjamin K.

doi:10.3115/1567564.1567566

Cited by 2 publications

(2 citation statements)

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“…in DUC), with substantial effort in Japanese (see NTCIR) and work on Chinese (Chan et al 2000), Dutch (Moens and Dumortier 2000) and German (Reithinger et al 2000), and both raw Arabic (Douzidia and Lapalme 2204) and automatically-translated Arabic news in DUC 2004. Systems that deploy NLP resources like dictionaries have to respond to language, but statistical systems need not or do it in minimal ways as in stemming.…”

Section: Languagementioning

confidence: 99%

Automatic summarising: The state of the art

Jones

2007

Information Processing & Management

257

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This paper reviews research on automatic summarising in the last decade. This work has grown, stimulated by technology and by evaluation programmes. The paper uses several frameworks to organise the review, for summarising itself, for the factors affecting summarising, for systems, and for evaluation.The review examines the evaluation strategies applied to summarising, the issues they raise, and the major programmes. It considers the input, purpose and output factors investigated in recent summarising research, and discusses the classes of strategy, extractive and non-extractive, that have been explored, illustrating the range of systems built.The conclusions drawn are that automatic summarisation has made valuable progress, with useful applications, better evaluation, and more task understanding. But summarising systems are still poorly motivated in relation to the factors affecting them, and evaluation needs taking much further to engage with the purposes summaries are intended to serve and the contexts in which they are used.

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Section: Languagementioning

confidence: 99%

Automatic summarising: The state of the art

Jones

2007

Information Processing & Management

257

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“…Sentences often leap incoherently from topic to topic, confusing the reader and hampering his ability to identify information of interest. Interest in producing textually coherent summaries has consequently increased in recent years, leading to a wide variety of approaches, including IRinfluenced techniques (Salton et al [1997]; Carbonell and Goldstein [1998]), variations on lexical chaining (Brunn et al [2002]; Karamuftuoglu [2002]), and discourse structure analysis (Marcu [1997(Marcu [ , 1999; Chan et al [2000]). Unfortunately, 0 The research described in this paper was carried out while the author was at the University of Toronto.…”

Section: Introductionmentioning

confidence: 99%

Latent semantic analysis and the construction of coherent extracts

Miller¹

2004

Recent Advances in Natural Language Processing III

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We describe a language-neutral automatic summarization system which aims to produce coherent extracts. It builds an initial extract composed solely of topic sentences, and then recursively fills in the topical lacunae by providing linking material between semantically dissimilar sentences. While experiments with human judges did not prove a statistically significant increase in textual coherence with the use of a latent semantic analysis module, we found a strong positive correlation between coherence and overall summary quality.

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Mining discourse markers for Chinese textual summarization

Cited by 2 publications

References 0 publications

Automatic summarising: The state of the art

Automatic summarising: The state of the art

Latent semantic analysis and the construction of coherent extracts

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