Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
The automated acquisition of topic signatures for text summarization
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Multidocument summarization via information extraction
HLT '01 Proceedings of the first international conference on Human language technology research
From single to multi-document summarization: a prototype system and its evaluation
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Answering complex questions with random walk models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Using random walks for question-focused sentence retrieval
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Automatic creation of domain templates
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Older versions of the ROUGEeval summarization evaluation system were easier to fool
Information Processing and Management: an International Journal
Topic-driven multi-document summarization with encyclopedic knowledge and spreading activation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
A novel discourse parser based on support vector machine classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
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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%.