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This paper explores alternate algorithms, reward functions and feature sets for performing multi-document summarization using reinforcement learning with a high focus on reproducibility. We show that ROUGE results can be improved using a unigram and bigram similarity metric when training a learner to select sentences for summarization. Learners are trained to summarize document clusters based on various algorithms and reward functions and then evaluated using ROUGE. Our experiments show a statistically significant improvement of 1.33%, 1.58%, and 2.25% for ROUGE-1, ROUGE-2 and ROUGEL scores, respectively, when compared with the performance of the state of the art in automatic summarization with reinforcement learning on the DUC2004 dataset. Furthermore query focused extensions of our approach show an improvement of 1.37% and 2.31% for ROUGE-2 and ROUGE-SU4 respectively over query focused extensions of the state of the art with reinforcement learning on the DUC2006 dataset.

Fear the REAPER: A System for Automatic Multi-Document Summarization with Reinforcement Learning