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Title: Deep Transfer Reinforcement Learning for Text Summarization
Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-ofthe-art results on a variety of datasets. Through an extensive set of experiments, we also show the ability of our proposed framework to fine-tune the text summarization model using only a few training samples. To the best of our knowledge, this is the first work that studies transfer learning in text summarization and provides a generic solution that works well on unseen data  more » « less
Award ID(s):
1838730 1707498 1619028
PAR ID:
10143406
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of SIAM International Conference on Data Mining (SDM)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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