论文标题
波斯键形的生成使用序列到序列模型
Persian Keyphrase Generation Using Sequence-to-Sequence Models
论文作者
论文摘要
钥匙拼是对输入文本的简短摘要，并提供了文本中讨论的主要主题。键形提取是一项有用的上游任务，可以在各种自然语言处理问题（例如文本摘要和信息检索）中使用。但是，并非所有键形词都在文本主体中明确提及。在现实世界中，总是有一些主题被隐式讨论。提取此类键形需要一种生成方法，这是在此采用的。在本文中，我们尝试使用深层序列到序列模型来解决键形生成和从新闻文章中提取的问题。这些模型在键形提取的任务中大大优于常规方法，例如主题等级，kpminer和kea。
Keyphrases are a very short summary of an input text and provide the main subjects discussed in the text. Keyphrase extraction is a useful upstream task and can be used in various natural language processing problems, for example, text summarization and information retrieval, to name a few. However, not all the keyphrases are explicitly mentioned in the body of the text. In real-world examples there are always some topics that are discussed implicitly. Extracting such keyphrases requires a generative approach, which is adopted here. In this paper, we try to tackle the problem of keyphrase generation and extraction from news articles using deep sequence-to-sequence models. These models significantly outperform the conventional methods such as Topic Rank, KPMiner, and KEA in the task of keyphrase extraction.
