Retrieve, Rerank and Rewrite: Soft Template Based Neural Summarization

ACL 2018  ·  Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei ·

Most previous seq2seq summarization systems purely depend on the source text to generate summaries, which tends to work unstably. Inspired by the traditional template-based summarization approaches, this paper proposes to use existing summaries as soft templates to guide the seq2seq model. To this end, we use a popular IR platform to Retrieve proper summaries as candidate templates. Then, we extend the seq2seq framework to jointly conduct template Reranking and template-aware summary generation (Rewriting). Experiments show that, in terms of informativeness, our model significantly outperforms the state-of-the-art methods, and even soft templates themselves demonstrate high competitiveness. In addition, the import of high-quality external summaries improves the stability and readability of generated summaries.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Text Summarization GigaWord Re^3 Sum ROUGE-1 37.04 # 19
ROUGE-2 19.03 # 17
ROUGE-L 34.46 # 21