Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization

اسناد مشابه

• Efficiently Computed Lexical Chains as an Intermediate Representation for Automatic Text Summarization

  مقاله‌ی دسترسی آزاد۲۰۰۲Computational LinguisticsH. GregorySilber, Kathleen F.McCoy

• BioChain: Using Lexical Chaining Methods for Biomedical Text Summarization

  سند علمی۲۰۰۵Lawrence Reeve

  1 ABSTRACT Lexical chaining is a technique for identifying semantically-related terms in a text. It is useful in document summarization in order to identify the top sentences most likely to contain the main ideas of a document or document set. These top sentences are then extract

• An Efficient Method based on Lexical Chains for Automatic Text Summarization

  سند علمی۲۰۱۶Shweta Saxena, Akash Saxena, Gregory Silber, Kathleen F. McCoy

  Automatic Text Summarization is an interesting topic for research. Still it is growing on. Increment of the data is exponentially growing on and it becomes too much difficult to find out the correct or relevant data in huge amount of data. So it becomes important for researchers

• Automatic Text Summarization Using Lexical Chains: Algorithms and Experiments

  سند علمی۲۰۰۸Maheedhar Kolla, Yllias Chali, Shahadat Hossain

  Summarization is a complex task that requires understanding of the document con­ tent to determine the importance of the text. Lexical cohesion is a method to identify connected portions of the text based on the relations between the words in the text. Lexical cohesive relations

• Computing Lexical Chains for Automatic Arabic Text Summarization

  سند علمی۲۰۱۵Hamza Zidoum

  Automatic Text Summarization has received a great deal of attention in the past couple of decades. It has gained a lot of interest especially with the proliferation of the Internet and the new technologies. Arabic as a language still lacks research in the field of Information Ret

• Using Genetic Algorithms with Lexical Chains for Automatic Text Summarization

  سند علمی۲۰۱۲ICAARTMine Berker, Tunga Güngör

  Automatic text summarization takes an input text and extracts the most important content in the text. Determining the importance of information depends on several factors. In this paper, we combine two different approaches that have been used in the text summarization domain. The