Syntax-driven sentence revision for broadcast news summarization

Tanaka, Hideki; Akinori, Kinoshita; Kobayakawa, Takeshi; Kumano, Tadashi; Katoh, Naoto

doi:10.3115/1708155.1708163

Cited by 28 publications

(15 citation statements)

References 9 publications

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“…Here the same chunks are searched in lead and body sentences [29]. Then these phrases are aligned using similarity metric.…”

Section: Methods For Extractive Summarizationmentioning

confidence: 99%

Trends in Extractive and Abstractive Techniques in Text Summarization

Bhatia¹,

Jaiswal²

2015

IJCA

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Text Summarization was proved to be an advantage over manually summarizing the large data. It condenses the salient features from the text by preserving the content and serves the meaningful summary. Classification can be done in two ways -extractive and abstractive summarization. Extractive summarization uses statistical and linguistic features to determine the important features and fuse them into a shorter version. Whereas abstractive summarization understands the whole document and then generates the summary. In this paper extractive and abstractive methods are framed.

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“…Here the same chunks are searched in lead and body sentences [29]. Then these phrases are aligned using similarity metric.…”

Section: Methods For Extractive Summarizationmentioning

confidence: 99%

Trends in Extractive and Abstractive Techniques in Text Summarization

Bhatia¹,

Jaiswal²

2015

IJCA

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“…In addition, the use of the hierarchical summaries reduces the number of bytes per user request by more than half. Tanaka et al [108] search for the same chunk in lead body sentence called triggers. Phrases are identified according to similarity for substitution of body phrase into the lead phrase.…”

Section: Lead and Body Phrasementioning

confidence: 99%

“…Systems like SEE(Summarization Evaluation Environment), ROUGE( RecallOriented Understudy for Gisting Evaluation ) or BE(Basic Elements)or BLEU 3 have been created to help to this task. The Pyramid method is a novel semi-automatic evaluation method [108]. Its basic idea is to identify summarization content units (SCUs) that are used for comparison of information in summaries.…”

Section: Intrinsic Evaluationmentioning

confidence: 99%

An Overview of Text Summarization

Rananavare¹,

Reddy²

2017

IJCA

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The amount of e-information available has increased greatly over the past few decades. As the vast amount of information is available for every theme on Internet, shortening the information in the form of summary would immensely benefit readers. Hence, the natural language processing research community is developing new methods for summarizing the text mechanically. Automatic text summarization system produces a summary, i.e. short length text that includes all the significant information for the article. This paper presents a comprehensive survey of contemporary text summarization of extractive and abstractive approaches.

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“…Jing and McKeown (7) found that human summarization can be traced back to cut-and-paste operations of a text and proposed a revision method consisting of sentence reduction and combination modules with a sentence extraction part. Hideki Tanaka et al [8] method did not use the coreference relation of noun phrases (NPs), but rather insertion and substitution of the phrases to modify the same head chunk in lead and other sentences. It addressed the problem of revising the lead sentence in a news text to increase the amount of the key information.…”

Section: Reorganization and Revisionmentioning

confidence: 99%

Automatic Abstractive Summarization Task for New Article

Helen¹

2018

emitter

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Understanding the contents of numerous articles requires strenuous effort. While manually reading the summary or abstract is one way, automatic summarization offers more efficient way in doing so. The current research in automatic summarization focuses on the statistical method and the Natural Processing Language (NLP) method. Statistical method produces Extractive Summary that the summaries consist of independent sentences considered important content of document. Unfortunately, the coherence of the summary is poor. Besides that, the Natural Processing Language expected can produces summary where sentences in summary should not be taken from sentences in the document, but come from the person making the summary. So, the summaries closed to human-summary, coherent and well structured. This research proposed Extractive summarization for news article about Coruption in Indonesia. We use five classes of important word/ phrase and make them in one sentence as summary. We find that there are still opportunities to develop better outcomes that are better coherence and better accuracy

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Syntax-driven sentence revision for broadcast news summarization

Cited by 28 publications

References 9 publications

Trends in Extractive and Abstractive Techniques in Text Summarization

Trends in Extractive and Abstractive Techniques in Text Summarization

An Overview of Text Summarization

Automatic Abstractive Summarization Task for New Article

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