@inproceedings{ma-etal-2017-improving,
    title = "Improving Semantic Relevance for Sequence-to-Sequence Learning of {C}hinese Social Media Text Summarization",
    author = "Ma, Shuming  and
      Sun, Xu  and
      Xu, Jingjing  and
      Wang, Houfeng  and
      Li, Wenjie  and
      Su, Qi",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-2100",
    doi = "10.18653/v1/P17-2100",
    pages = "635--640",
    abstract = "Current Chinese social media text summarization models are based on an encoder-decoder framework. Although its generated summaries are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization. We introduce a Semantic Relevance Based neural 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, the similarity score between the representations is maximized during training. Our experiments show that the proposed model outperforms baseline systems on a social media corpus.",
}