Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 221–229 Brussels, Belgium, October 31 - November 1, 2018. c⃝2018 Association for Computational Linguistics 221 Simple Unsupervised Keyphrase Extraction using Sentence Embeddings Kamil Bennani-Smires1, Claudiu Musat1, Andreaa Hossmann1, Michael Baeriswyl1, Martin Jaggi2 1Data, Analytics & AI, Swisscom AG firstname.lastname@swisscom.com 2Machine Learning and Optimization Laboratory, EPFL martin.jaggi@epfl.ch Abstract Keyphrase extraction is the task of automat- ically selecting a small set of phrases that best describe a given free text document. Su- pervised keyphrase extraction requires large amounts of labeled training data and gener- alizes very poorly outside the domain of the training data. At the same time, unsuper- vised systems have poor accuracy, and often do not generalize well, as they require the in- put document to belong to a larger corpus also given as input. Addressing these drawbacks, in this paper, we tackle keyphrase extrac- tion from single documents with EmbedRank: a novel unsupervised method, that leverages sentence embeddings. EmbedRank achieves higher F-scores than graph-based state of the art systems on standard datasets and is suit- able for real-time processing of large amounts of Web data. With EmbedRank, we also ex- plicitly increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study includ- ing over 200 votes showed that, although re- ducing the phrases’ semantic overlap leads to no gains in F-score, our high diversity selec- tion is preferred by humans. 1 Introduction Document keywords and keyphrases enable faster and more accurate search in large text collections, serve as condensed document summaries, and are used for various other applications, such as cate- gorization of documents. In particular, keyphrase extraction is a crucial component when gleaning real-time insights from large amounts of Web and social media data. In this case, the extraction must be fast and the keyphrases must be disjoint. Most existing systems are slow and plagued by over- generation, i.e. extracting redundant keyphrases. Here, we address both these problems with a new unsupervised algorithm. Unsupervised keyphrase extraction has a series of advantages over supervised methods. Super- vised keyphrase extraction always requires the ex- istence of a (large) annotated corpus of both doc- uments and their manually selected keyphrases to train on - a very strong requirement in most cases. Supervised methods also perform poorly outside of the domain represented by the training corpus - a big issue, considering that the domain of new documents may not be known at all. Unsupervised keyphrase extraction addresses such information- constrained situations in one of two ways: (a) by relying on in-corpus statistical information (e.g., the inverse document frequency of the words), and the current document; (b) by only using informa- tion extracted from the current document. We propose EmbedRank - an unsupervised method to automatically extract keyphrases from a document, that is both simple and only requires the current document itself, rather than an entire corpus that this document may be linked to. Our method relies on …

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