Latent Keyphrase Extraction Using LDA Model

As the number of document resources is continuously increasing, automatically extracting keyphrases from a document becomes one of the main issues in recent days. However, most previous works have tried to extract keyphrases from words in documents, so they overlooked latent keyphrases which did not appear in documents. Although latent keyphrases do not appear in documents, they can undertake an important role in text summarization and information retrieval because they implicate meaningful concepts or contents of documents. Also, they cover more than one fourth of the entire keyphrases in the real-world datasets and they can be utilized in short articles such as SNS which rarely have explicit keyphrases. In this paper, we propose a new approach that selects candidate keyphrases from the keyphrases of neighbor documents which are similar to the given document and evaluates the importance of the candidates with the individual words in the candidates. Experiment result shows that latent keyphrases can be extracted at a reasonable level.