yuewang-cuhk/TAKG
The official implementation of ACL 2019 paper "Topic-Aware Neural Keyphrase Generation for Social Media Language"
This project helps social media analysts, content strategists, or community managers automatically identify the most important topics and keywords from large volumes of social media posts. You feed it raw text from platforms like Twitter, Weibo, or StackExchange, and it generates a list of relevant keyphrases. This is for anyone who needs to quickly understand the main subjects being discussed in user-generated content.
153 stars. No commits in the last 6 months.
Use this if you need to generate concise, topic-aware keyphrases for social media content, especially when the keyphrases might not directly appear in the original text.
Not ideal if you only need to extract existing words as keywords, or if your content isn't social media language and might have different underlying topic structures.
Stars
153
Forks
31
Language
Python
License
MIT
Category
Last pushed
Aug 05, 2019
Commits (30d)
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