Lexical cohesion based topic modeling for summarization


Creative Commons License

Ercan G., Cicekli I.

9th International Conference on Intelligent Text Processing and Computational Linguistics, Haifa, İsrail, 17 - 23 Şubat 2008, cilt.4919, ss.582-592 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 4919
  • Doi Numarası: 10.1007/978-3-540-78135-6_50
  • Basıldığı Şehir: Haifa
  • Basıldığı Ülke: İsrail
  • Sayfa Sayıları: ss.582-592
  • Hacettepe Üniversitesi Adresli: Hayır

Özet

In this paper, we attack the problem of forming extracts for text summarization. Forming extracts involves selecting the most representative and significant sentences from the text. Our method takes advantage of the lexical cohesion structure in the text in order to evaluate significance of sentences. Lexical chains have been used in summarization research to analyze the lexical cohesion structure and represent topics in a text. Our algorithm represents topics by sets of co-located lexical chains to take advantage of more lexical cohesion clues. Our algorithm segments the text with respect to each topic and finds the most important topic segments. Our summarization algorithm has achieved better results, compared to some other lexical chain based algorithms.