Weakly Supervised Natural Language Processing Framework for Abstractive Multi-Document Summarization: Weakly Supervised Abstractive Multi-Document Summarization

Abstract

In this paper, we propose a new weakly supervised abstractive news summarization framework using pattern based approaches. Our system first generates meaningful patterns from sentences. Then, in order to precisely cluster patterns, we propose a novel semisupervised pattern learning algorithm that leverages a hand-crafted list of topic-relevant keywords, which are the only weakly supervised information used by our framework to generate aspect-oriented summarization. After that, our system generates new patterns by fusing existing patterns and selecting top ranked new patterns via the recurrent neural network language model. Finally, we introduce a new pattern based surface realization algorithm to generate abstractive summaries. Automatic and manual evaluations demonstrate the effectiveness and advantages of our new methods. Code is available at: https://github.com/jerryli1981

DOI: 10.1145/2806416.2806494

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Cite this paper

@inproceedings{Li2015WeaklySN, title={Weakly Supervised Natural Language Processing Framework for Abstractive Multi-Document Summarization: Weakly Supervised Abstractive Multi-Document Summarization}, author={Peng Li and Tom Weidong Cai and Heng Huang}, booktitle={CIKM}, year={2015} }