Seq2seq learning has produced promising results on summarization. However, in many cases, system summaries still struggle to keep the meaning of the original intact. They may miss out important words or relations that play critical roles in the syntactic structure of source sentences. In this paper, we present structure-infused copy mechanisms to facilitate copying important words and relations from the source sentence to summary sentence. The approach naturally combines source dependency structure with the copy mechanism of an abstractive sentence summarizer. Experimental results demonstrate the effectiveness of incorporating source-side syntactic information in the system, and our proposed approach compares favorably to state-of-the-art methods.
@article{arxiv.1806.05658,
title = {Structure-Infused Copy Mechanisms for Abstractive Summarization},
author = {Kaiqiang Song and Lin Zhao and Fei Liu},
journal= {arXiv preprint arXiv:1806.05658},
year = {2018}
}