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Automatic labeling of semantic roles
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Tailoring Text Using Topic Words: Selection and Compression
DEXA '02 Proceedings of the 13th International Workshop on Database and Expert Systems Applications
Statistics-Based Summarization - Step One: Sentence Compression
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Wordnet-based inference of textual context, cohesion and coherence
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Head-driven statistical models for natural language parsing
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Computational Linguistics
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Computational Linguistics
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HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
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EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
UTD-SRL: a pipeline architecture for extracting frame semantic structures
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Semantic role labeling using complete syntactic analysis
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Information Processing and Management: an International Journal
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Expert Systems with Applications: An International Journal
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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The problem of using topic representations for multidocument summarization (MDS) has received considerable attention recently. Several topic representations have been employed for producing informative and coherent summaries. In this article, we describe five previously known topic representations and introduce two novel representations of topics based on topic themes. We present eight different methods of generating multidocument summaries and evaluate each of these methods on a large set of topics used in past DUC workshops. Our evaluation results show a significant improvement in the quality of summaries based on topic themes over MDS methods that use other alternative topic representations.