Multi-Document Summarization - Pachlor, Rohit
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The world is overloaded with information due to the internet revolution. This calls for an efficient and accurate summarization system to extract relevant information. Text summarization system automatically generates a summary of a given document and helps people to take effective decisions in less time. In this book two methods have been proposed for query-focused multi-document summarization that uses k-mean clustering and term-frequency-inverse-sentence-frequency method for sentence weighting to rank the sentences of the document(s) with respect to a given query. The proposed methods find…mehr

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Produktbeschreibung
The world is overloaded with information due to the internet revolution. This calls for an efficient and accurate summarization system to extract relevant information. Text summarization system automatically generates a summary of a given document and helps people to take effective decisions in less time. In this book two methods have been proposed for query-focused multi-document summarization that uses k-mean clustering and term-frequency-inverse-sentence-frequency method for sentence weighting to rank the sentences of the document(s) with respect to a given query. The proposed methods find the proximity of documents and query, and later uses this proximity to rank sentences of each document. A comparative study for proposed methods has been done and experimental results shows that both methods are comparable because of a slight difference in performance. DUC 2007 test dataset and ROUGH-1.5.5 summarization evaluation package is used for evaluation purpose.
Autorenporträt
Rohit Pachlor is currently working as an Assistant Professor in the department of Computer Engineering, Narsee Monjee Institute of Management Studies (NMIMS), India. He pursued his M.Tech in Computer Science and Engineering from Maulana Azad National Institute of Technology, Bhopal (India). His broad area of research is text mining.