A Hybrid Word-Character Model for Abstractive Summarization

02/27/2018

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by   Chieh-Teng Chang, et al.

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Abstractive summarization is the popular research topic nowadays. Due to the difference in language property, Chinese summarization also gains lots of attention. Most of studies use character-based representation instead of word-based to keep out the error introduced by word segmentation and OOV problem. However, we believe that word-based representation can capture the semantics of the articles more accurately. We proposed a hybrid word-character model preserves the advantage of both word-based and character-based representations. Our method also enables us to use larger word vocabulary size than anyone else. We call this new method HWC (Hybrid Word-Character). We conduct the experiments on LCSTS Chinese summarization dataset, and out-perform the current state-of-the-art by at least 8 ROUGE points.