Terms derived from frequent sequences for extractive text summarization

Yulia Ledeneva, Alexander Gelbukh, René Arnulfo García-Hernández

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

36 Citas (Scopus)

Resumen

Automatic text summarization helps the user to quickly understand large volumes of information. We present a language- and domain-independent statistical-based method for single-document extractive summarization, i.e., to produce a text summary by extracting some sentences from the given text. We show experimentally that words that are parts of bigrams that repeat more than once in the text are good terms to describe the text's contents, and so are also so-called maximal frequent sentences. We also show that the frequency of the term as term weight gives good results (while we only count the occurrences of a term in repeating bigrams).

Idioma originalInglés
Título de la publicación alojadaComputational Linguistics and Intelligent Text Processing - 9th International Conference, CICLing 2008, Proceedings
Páginas593-604
Número de páginas12
DOI
EstadoPublicada - 2008
Evento9th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2008 - Haifa, Israel
Duración: 17 feb. 200823 feb. 2008

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen4919 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia9th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2008
País/TerritorioIsrael
CiudadHaifa
Período17/02/0823/02/08

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