Single Document Summarization Based on Local Topic Identification and Word Frequency

Teng, Zhi; Liu, Ye; Ren, Fuji; Tsuchiya, Seiji

doi:10.1109/micai.2008.12

Cited by 19 publications

(17 citation statements)

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“…Moreover Teng, et al [5] propose an approach which combines the automatic topics identification technique with the terms frequency method. This methodology consists of calculating initially the similarity between the sentences, then carry out the identification of the subject covered by gathering similar sentences in clusters.…”

Section: Topic-based Approachesmentioning

confidence: 99%

Untitled

2015

Journal of Asian Scientific Research

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Section: Topic-based Approachesmentioning

confidence: 99%

Untitled

2015

Journal of Asian Scientific Research

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“…Following this idea, in Harabagiu and Lacatusu (2005), it was analysed how the structure of a document is characterised in terms of topics' themes, which are representations of events that are reiterated throughout the document collection, and therefore represent repetitive information. Furthermore, in Teng et al (2008), a singledocument summarisation approach is suggested, which combines local topic identification with term frequency. The proposed methodology first computes the sentence similarity, and then performs the topic identification by doing sentence clustering.…”

Section: The Process Of Summarisation From a Computational Perspectivementioning

confidence: 99%

COMPENDIUM: a text summarisation tool for generating summaries of multiple purposes, domains, and genres

Lloret¹,

Palomar²

2012

Nat. Lang. Eng.

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In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, single-and multi-document summaries can be produced; as the output, the summaries can be extractive or abstractiveoriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

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“…A document refers to piece of text. Categories may be derived from a sparse classification scheme or from a large collection of very specific text documents [20]. Categories may be represented numerically or using single word or phrase or words with senses, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Many summarization techniques and their evaluation methods have been developed for this purpose. Such techniques are RANDOM [20], LEAD [20], MEAD [21] and PYTHY [22] etc. which are used to generate the summary.…”

Section: Introductionmentioning

confidence: 99%

“…1 Formal IR Model System [3] Tokenization process is an integral part of IR systems, involves pre-processing of given documents and generates respective tokens. In some, tokenization techniques count of token were used to establish a value "Word Count or Token Count" which is used in indexing/ranking process of document [20] [21]. A typical structure of tokenization process is explained in figure 2.…”

Section: Introductionmentioning

confidence: 99%

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