Abstract
Recently, there has been increased interest in topic-focused multi-document summarization where the task is to produce automatic summaries in response to a given topic or specific information requested by the user. In this paper, we incorporate a deeper semantic analysis of the source documents to select important concepts by using a predefined list of important aspects that act as a guide for selecting the most relevant sentences into the summaries. We exploit these aspects and build a novel methodology for topic-focused multi-document summarization that operates on a Markov chain tuned to extract the most important sentences by following a random walk paradigm. Our evaluations suggest that the augmentation of important aspects with the random walk model can raise the summary quality over the random walk model up to 19.22%.
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Chali, Y., Hasan, S.A., Imam, K. (2011). An Aspect-Driven Random Walk Model for Topic-Focused Multi-document Summarization. In: Salem, M.V.M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds) Information Retrieval Technology. AIRS 2011. Lecture Notes in Computer Science, vol 7097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25631-8_35
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DOI: https://doi.org/10.1007/978-3-642-25631-8_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25630-1
Online ISBN: 978-3-642-25631-8
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