Unsupervised Keyphrase Extraction via Interpretable Neural Networks
This addresses the problem of extracting keyphrases without heuristics for researchers and practitioners in NLP, offering a novel method that is incremental in improving existing approaches.
The paper tackles unsupervised keyphrase extraction by defining keyphrases as phrases salient for predicting document topics, using a self-explaining model called INSPECT. It achieves state-of-the-art results on four datasets across scientific publications and news articles.
Keyphrase extraction aims at automatically extracting a list of "important" phrases representing the key concepts in a document. Prior approaches for unsupervised keyphrase extraction resorted to heuristic notions of phrase importance via embedding clustering or graph centrality, requiring extensive domain expertise. Our work presents a simple alternative approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. To this end, we propose INSPECT -- an approach that uses self-explaining models for identifying influential keyphrases in a document by measuring the predictive impact of input phrases on the downstream task of the document topic classification. We show that this novel method not only alleviates the need for ad-hoc heuristics but also achieves state-of-the-art results in unsupervised keyphrase extraction in four datasets across two domains: scientific publications and news articles.