Kavli Affiliate: Xiang Zhang
| First 5 Authors: Puyuan Liu, Xiang Zhang, Lili Mou, ,
| Summary:
Sentence summarization aims at compressing a long sentence into a short one
that keeps the main gist, and has extensive real-world applications such as
headline generation. In previous work, researchers have developed various
approaches to improve the ROUGE score, which is the main evaluation metric for
summarization, whereas controlling the summary length has not drawn much
attention. In our work, we address a new problem of explicit character-level
length control for summarization, and propose a dynamic programming algorithm
based on the Connectionist Temporal Classification (CTC) model. Results show
that our approach not only achieves higher ROUGE scores but also yields more
complete sentences.
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