结合邻域知识的文档级关键词抽取方法

基于编码器-解码器(encoder-decoder)框架的生成式方法在关键词抽取任务上得到了广泛应用并取得了较好的性能,然而该方法面临的主要挑战为建模有效的文档向量表示,及生成覆盖整个文档主题的关键词集合,这些挑战都会直接影响关键词抽取的结果.该文提出了结合邻域知识的文档级关键词抽取模型以应对这些挑战.具体来说,通过给...

Document-level Keyphrase Extraction Approach using Neighborhood Knowledge

Encoder-decoder-based generative approaches have been widely used and achieved good performance for keyphrase extraction tasks. However, the main challenges of the encoder-decoder-based approach are modeling an effective document vector representation and generating a set of keyphrases covering the entire document topic, which can directly affect the keyphrase extraction results. In this paper, a document-level keyphrase extraction model incorporating neighborhood knowledge is proposed to address the challenges mentioned above simultaneously. Specifically, the original document is extending to a document set by adding some nearest-neighbor documents. Then, each document in the set is constructed into a word graph based on the distance between words, and all the word graphs in the set are merged into a large graph, which is then encoded using graph convolutional networks. Besides, to fully cover diverse keyphrases and topics, the context modification mechanism and coverage mechanism are introduced at the decoding step. Finally, by comparing with the existing baseline model on four benchmark datasets, the experimental results show that the method can effectively improve the performance of extracting keyphrases.