Automatic Summarization

Nenkova, Ani

doi:10.1561/1500000015

Cited by 333 publications

(214 citation statements)

References 129 publications

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“…Much of the work we reviewed in the previous sections involved generic summarization whereby the relevance of a summary is decided just based on the input document without relating to its domain or the user needs (Nenkova and McKeown, 2011). For example, inputs such as medical documents, news documents or emails; have special structures or unique characteristics which should be taken into account by the summarizer to produce more accurate information.…”

Section: Domain Specific Summarizationmentioning

confidence: 99%

“…Since then, various techniques have been successfully used to extract the important contents from text document to represent document summary (Gupta and Lehal, 2010;Nenkova and McKeown, 2011;Saggion and Poibeau, 2013). The aim of automatic text summarization is similar to the reason why we humans create summaries; i.e., to produce a shorter representation of the original text.…”

Section: Introductionmentioning

confidence: 99%

“…The majority of the work that has been done revolves around generic summarization (Nenkova and McKeown, 2011). There have also been developments of summarization systems which are centred upon various domain of interest.…”

Section: Introductionmentioning

confidence: 99%

“…The queries are typically natural language questions or keywords that are related to a particular subject. For instance, snippets produced by search engines is an example of query-based application (Nenkova and McKeown, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Domain Specific Summarizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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“…Text summarisation [15,19] is an information reduction process, where the aim is to identify the important information within a large document, or set of documents, and infer an essential subset of the textual content for user consumption. Examples of text summarisation being applied to assist with user's information needs include search engine results pages, where snippets of relevant pages are shown, and online news portals, where extracts of newswire documents are shown.…”

Section: Introductionmentioning

confidence: 99%

Experiments in Newswire Summarisation

Mackie

McCreadie

Macdonald

et al. 2016

Lecture Notes in Computer Science

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Abstract. In this paper, we investigate extractive multi-document summarisation algorithms over newswire corpora. Examining recent findings, baseline algorithms, and state-of-the-art systems is pertinent given the current research interest in event tracking and summarisation. We first reproduce previous findings from the literature, validating that automatic summarisation evaluation is a useful proxy for manual evaluation, and validating that several state-of-the-art systems with similar automatic evaluation scores create different summaries from one another. Following this verification of previous findings, we then reimplement various baseline and state-of-the-art summarisation algorithms, and make several observations from our experiments. Our findings include: an optimised Lead baseline; indication that several standard baselines may be weak; evidence that the standard baselines can be improved; results showing that the most effective improved baselines are not statistically significantly less effective than the current state-of-the-art systems; and finally, observations that manually optimising the choice of anti-redundancy components, per topic, can lead to improvements in summarisation effectiveness.

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2014

Automatic Text Summarization

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Automatic Summarization

Cited by 333 publications

References 129 publications

A Review on Automatic Text Summarization Approaches

A Review on Automatic Text Summarization Approaches

Experiments in Newswire Summarisation

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