Abstract
Keyphrase provides highly-summative information that can be effectively used for understanding, organizing and retrieving text content. Though previous studies have provided many workable solutions for automated keyphrase extraction, they commonly divided the to-be-summarized content into multiple text chunks, then ranked and selected the most meaningful ones. These approaches could neither identify keyphrases that do not appear in the text, nor capture the real semantic meaning behind the text. We propose a generative model for keyphrase prediction with an encoder-decoder framework, which can effectively overcome the above drawbacks. We name it as deep keyphrase generation since it attempts to capture the deep semantic meaning of the content with a deep learning method. Empirical analysis on six datasets demonstrates that our proposed model not only achieves a significant performance boost on extracting keyphrases that appear in the source text, but also can generate absent keyphrases based on the semantic meaning of the text. Code and dataset are available at https://github.com/memray/seq2seq-keyphrase.
Highlights
A keyphrase or keyword is a piece of short, summative content that expresses the main semantic meaning of a longer text
This is the same as the keyphrase extraction task in prior studies, in which we analyze how well our proposed model performs on a commonly-defined task
We proposed an recurrent neural networks (RNN)-based generative model for predicting keyphrases in scientific text
Summary
A keyphrase or keyword is a piece of short, summative content that expresses the main semantic meaning of a longer text. The typical use of a keyphrase or keyword is in scientific publications to provide the core information of a paper. We use Automatically extracting keyphrases from a document is called keypharase extraction, and it has been widely used in many applications, such as information retrieval (Jones and Staveley, 1999), text summarization (Zhang et al, 2004), text categorization (Hulth and Megyesi, 2006), and opinion mining (Berend, 2011). Most of the existing keyphrase extraction algorithms have addressed this problem through two steps (Liu et al, 2009; Tomokiyo and Hurst, 2003). The first step is to acquire a list of keyphrase candidates. The second step is to rank candidates on their importance to the document, either through supervised or unsupervised machine learning methods with a set of manually-defined features (Frank et al, 1999; Liu et al, 2009, 2010; Kelleher and Luz, 2005; Matsuo and Ishizuka, 2004; Mihalcea and Tarau, 2004; Song et al, 2003; Witten et al, 1999)
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