Keyphrase extraction aims at automatically extracting a list of ``important” phrases representing the key concepts in a document. Prior approaches for unsupervised keyphrase extraction resort to heuristic notions of phrase importance via embedding similarities or graph centrality, requiring extensive domain expertise to develop them. Our work presents an alternative operational definition: phrases that are most useful for predicting the topic of a text are keyphrases. To this end, we propose INSPECT—a self-explaining neural framework for identifying influential keyphrases by measuring the predictive impact of input phrases on the downstream task of topic classification.
We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB). In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus. At each step the operation uses a combination of sparse-matrix TFIDF indices and maximum inner product search (MIPS) on a special index of contextual representations.