{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/improving-semantic-relevance-for-sequence-to","title":"Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization","arxiv_id":"1706.02459","date":"2017-06-08","proceeding":"ACL 2017 7","authors":["Shuming Ma","Xu sun","Jingjing Xu","Houfeng Wang","Wenjie Li","Qi Su"],"abstract":"Current Chinese social media text summarization models are based on an\nencoder-decoder framework. Although its generated summaries are similar to\nsource texts literally, they have low semantic relevance. In this work, our\ngoal is to improve semantic relevance between source texts and summaries for\nChinese social media summarization. We introduce a Semantic Relevance Based\nneural model to encourage high semantic similarity between texts and summaries.\nIn our model, the source text is represented by a gated attention encoder,\nwhile the summary representation is produced by a decoder. Besides, the\nsimilarity score between the representations is maximized during training. Our\nexperiments show that the proposed model outperforms baseline systems on a\nsocial media corpus.","url_abs":"http://arxiv.org/abs/1706.02459v1","url_pdf":"http://arxiv.org/pdf/1706.02459v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"improving-semantic-relevance-for-sequence-to","repo_url":"https://github.com/shumingma/SRB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"semantic-similarity","task_name":"Semantic Similarity"},{"task_slug":"semantic-textual-similarity","task_name":"Semantic Textual Similarity"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.02459","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}