LMLSTM:Extract Event-Oriented Keyphrase From News Stream

Keyphrase extraction, as a basis for many natural language processing and information retrieval tasks, can help people efficiently discover their interested information from vast streams of online documents. Previous methods are mostly proposed in general purpose, where keyphrases that represent the main topics are extracted. However, such keyphrases can hardly distinguish events from massive streams of long text documents that share similar topics and contain highly redundant information. In this paper, we address the task of keyphrase extraction for event-oriented retrieval. We propose a novel Long Short-Term Memory Network Language Model (LMLSTM) to extract event-oriented keyphrases that represent or related to a particular event. We conduct a series of experiments on a real-world dataset. The experimental results demonstrate the better performance of our approach than other state-of-the-art baselines.