Multi-document Summarization using Tensor Decomposition
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Redalyc
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| Formato: | Artículo científico |
| Lenguaje: | en |
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Instituto Politécnico Nacional
2014
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| _version_ | 1876422971641298944 |
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| author | Marina Litvak |
| author_facet | Marina Litvak |
| contents | Multi-document Summarization using Tensor Decomposition Marina Litvak Natalia Vanetik Computación multilingual multi Tensor decomposition focument summarization The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as possible, when compared to the original document set. We construct a new tensor-based representation that describes the given document set in terms of its topics. We then rank topics via Tensor Decomposition, and compile a summary from the sen- tences of the highest ranked topics. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067012 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| format | Artículo científico |
| id | redalyc_61532067012 |
| institution | Redalyc |
| language | en |
| publishDate | 2014 |
| publisher | Instituto Politécnico Nacional |
| spellingShingle | Multi-document Summarization using Tensor Decomposition Marina Litvak Computación multilingual multi Tensor decomposition focument summarization Multi-document Summarization using Tensor Decomposition Marina Litvak Natalia Vanetik Computación multilingual multi Tensor decomposition focument summarization The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as possible, when compared to the original document set. We construct a new tensor-based representation that describes the given document set in terms of its topics. We then rank topics via Tensor Decomposition, and compile a summary from the sen- tences of the highest ranked topics. 2014 artículo científico 1405-5546 https://www.redalyc.org/articulo.oa?id=61532067012 en http://www.redalyc.org/revista.oa?id=615 Computación y Sistemas application/pdf Instituto Politécnico Nacional Computación y Sistemas (México) Num.3 Vol.18 |
| title | Multi-document Summarization using Tensor Decomposition |
| topic | Computación multilingual multi Tensor decomposition focument summarization |
| url | https://www.redalyc.org/articulo.oa?id=61532067012 |