Year: 2018
Type: conference-paper
Abstract: We propose a unified model combining the strength of extractive and abstractive summarization.On the one hand, a simple extractive model can obtain sentence-level attention with high ROUGE scores but less readable.On the other hand, a more complicated abstractive model can obtain word-level dynamic attention to generate a more readable paragraph.In our model, sentence-level attention is used to modulate the word-level attention such that words in less attended sentences are less likely to be generated.Moreover, a novel inconsistency loss function is introduced to penalize the inconsistency between two levels of attentions.By end-to-end training our model with the inconsistency loss and original losses of extractive and abstractive models, we achieve state-of-theart ROUGE scores while being the most informative and readable summarization on the CNN/Daily Mail dataset in a solid human evaluation.
DOI: 10.18653/v1/p18-1013
Authors: Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, Kerui Min, Jing Tang, Min Sun
Institutions: National Tsing Hua University, Kingsoft (China)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: National Taiwan University
OpenAlex ID: W2963545005