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- W4285820128 abstract "AbstractAutomatic keyphrase generation can help human efficiently understand or process critical information from massive social media posts. Seq2Seq-based generation models that can produce both present and absent keyphrases have achieved remarkable performance. However, existing models are limited by the sparseness of posts that are widely exhibited in social media language. Sparseness makes it difficult for models to obtain useful features and generate accurate keyphrases. To address this problem, we propose the Topic and Reference Guided Keyphrase Generation model(TRGKG) for social media posts, which enrich scarce posts features by corpus-level topics and post-level reference knowledge. The proposed model incorporates a contextual neural topic model to exploit topics and a heterogeneous graph to capture reference knowledge from retrieved related posts. To guide the decoding process, we introduce new topic-aware hierarchical attention and copy mechanism, which directly copies appropriate words from both the source post and its references. Experiments on two public datasets demonstrate that TRGKG achieves state-of-the-art performance.KeywordsKeyphrase generationSeq2SeqNeural topic modelHeterogeneous graph" @default.
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- W4285820128 date "2022-01-01" @default.
- W4285820128 modified "2023-10-16" @default.
- W4285820128 title "Topic and Reference Guided Keyphrase Generation from Social Media" @default.
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- W4285820128 doi "https://doi.org/10.1007/978-3-031-10986-7_12" @default.
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