Log In Sign Up

KPDrop: An Approach to Improving Absent Keyphrase Generation

by   Seoyeon Park, et al.

Keyphrase generation is the task of generating phrases (keyphrases) that summarize the main topics of a given document. The generated keyphrases can be either present or absent from the text of the given document. While the extraction of present keyphrases has received much attention in the past, only recently a stronger focus has been placed on the generation of absent keyphrases. However, generating absent keyphrases is very challenging; even the best methods show only a modest degree of success. In this paper, we propose an approach, called keyphrase dropout (or KPDrop), to improve absent keyphrase generation. We randomly drop present keyphrases from the document and turn them into artificial absent keyphrases during training. We test our approach extensively and show that it consistently improves the absent performance of strong baselines in keyphrase generation.


page 1

page 2

page 3

page 4


Focused Attention Improves Document-Grounded Generation

Document grounded generation is the task of using the information provid...

Zero-shot topic generation

We present an approach to generating topics using a model trained only f...

Unsupervised Deep Keyphrase Generation

Keyphrase generation aims to summarize long documents with a collection ...

A Large-Scale Dataset for Biomedical Keyphrase Generation

Keyphrase generation is the task consisting in generating a set of words...

Generating Diverse Numbers of Diverse Keyphrases

Existing keyphrase generation studies suffer from the problems of genera...

Keyphrase Generation Beyond the Boundaries of Title and Abstract

Keyphrase generation aims at generating phrases (keyphrases) that best d...

Document Similarity for Texts of Varying Lengths via Hidden Topics

Measuring similarity between texts is an important task for several appl...