Update Summarization Based on Novel Topic Distribution

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F09%3A00502156" target="_blank" >RIV/49777513:23520/09:00502156 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Update Summarization Based on Novel Topic Distribution
Original language description
This paper deals with our recent research in text summarization. The field has moved from multi-document summarization to update summarization. When producing an update summary of a set of topic-related documents the summarizer assumes prior knowledge ofthe reader determined by a set of older documents of the same topic. The update summarizer thus must solve a novelty vs. redundancy problem. We describe the development of our summarizer which is based on Iterative Residual Rescaling (IRR) that createsthe latent semantic space of a set of documents under consideration. IRR generalizes Singular Value Decomposition (SVD) and enables to control the influence of major and minor topics in the latent space. Our sentence-extractive summarization method computes the redundancy, novelty and significance of each topic. These values are finally used in the sentence selection process. The sentence selection component prevents inner summary redundancy.
Czech name
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Czech description
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Project
<a href="/en/project/2C06009" target="_blank" >2C06009: Complex knowledge base tools for natural language communication with the semantic web</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2009
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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