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Current Chinese social media text summarization models are based on anencoder-decoder framework. Although its generated summaries are similar tosource texts literally, they have low semantic relevance. In this work, ourgoal is to improve semantic relevance between source texts and summaries forChinese social media summarization. We introduce a Semantic Relevance Basedneural model to encourage high semantic similarity between texts and summaries.In our model, the source text is represented by a gated attention encoder,while the summary representation is produced by a decoder. Besides, thesimilarity score between the representations is maximized during training. Ourexperiments show that the proposed model outperforms baseline systems on asocial media corpus.

Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization