Abstract
The goal of keyword extraction is to extract from a text, words, or phrases indicative of what it is talking about. In this work, we look at keyword extraction from a number of different perspectives: Statistics, Automatic Term Indexing, Information Retrieval (IR), Natural Language Processing (NLP), and the emerging Neural paradigm. The 1990s have seen some early attempts to tackle the issue primarily based on text statistics [13, 17]. Meanwhile, in IR, efforts were largely led by DARPA’s Topic Detection and Tracking (TDT) project [2]. In this contribution, we discuss how past innovations paved a way for more recent developments, such as LDA, PageRank, and Neural Networks. We walk through the history of keyword extraction over the last 50 years, noting differences and similarities among methods that emerged during the time. We conduct a large meta-analysis of the past literature using datasets from news media, science, and medicine to business and bureaucracy, to draw a general picture of what a successful approach would look like.
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