Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Improving summarization performance by sentence compression: a pilot study
AsianIR '03 Proceedings of the sixth international workshop on Information retrieval with Asian languages - Volume 11
A formal model for information selection in multi-sentence text extraction
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Fast gradient-descent methods for temporal-difference learning with linear function approximation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-document summarization by sentence extraction
NAACL-ANLP-AutoSum '00 Proceedings of the 2000 NAACL-ANLP Workshop on Automatic Summarization
Text summarization model based on maximum coverage problem and its variant
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
A study of global inference algorithms in multi-document summarization
ECIR'07 Proceedings of the 29th European conference on IR research
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We present a new approach to the problem of automatic text summarization called Automatic Summarization using Reinforcement Learning (ASRL) in this paper, which models the process of constructing a summary within the framework of reinforcement learning and attempts to optimize the given score function with the given feature representation of a summary. We demonstrate that the method of reinforcement learning can be adapted to automatic summarization problems naturally and simply, and other summarizing techniques, such as sentence compression, can be easily adapted as actions of the framework. The experimental results indicated ASRL was superior to the best performing method in DUC2004 and comparable to the state of the art ILP-style method, in terms of ROUGE scores. The results also revealed ASRL can search for sub-optimal solutions efficiently under conditions for effectively selecting features and the score function.