Multi-Document Summarization: Query-based Summarization - Rohit Pachlor
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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- ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Rohit Pachlor |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2013 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783659453229 |