  Automatic abstractive text summarization is an important and challenging
research topic of natural language processing. Among many widely used
languages, the Chinese language has a special property that a Chinese character
contains rich information comparable to a word. Existing Chinese text
summarization methods, either adopt totally character-based or word-based
representations, fail to fully exploit the information carried by both
representations. To accurately capture the essence of articles, we propose a
hybrid word-character approach (HWC) which preserves the advantages of both
word-based and character-based representations. We evaluate the advantage of
the proposed HWC approach by applying it to two existing methods, and discover
that it generates state-of-the-art performance with a margin of 24 ROUGE points
on a widely used dataset LCSTS. In addition, we find an issue contained in the
LCSTS dataset and offer a script to remove overlapping pairs (a summary and a
short text) to create a clean dataset for the community. The proposed HWC
approach also generates the best performance on the new, clean LCSTS dataset.
