DeepAI AI Chat
Log In Sign Up

Generating Diverse Numbers of Diverse Keyphrases

by   Xingdi Yuan, et al.
University of Pittsburgh

Existing keyphrase generation studies suffer from the problems of generating duplicate phrases and deficient evaluation based on a fixed number of predicted phrases. We propose a recurrent generative model that generates multiple keyphrases sequentially from a text, with specific modules that promote generation diversity. We further propose two new metrics that consider a variable number of phrases. With both existing and proposed evaluation setups, our model demonstrates superior performance to baselines on three types of keyphrase generation datasets, including two newly introduced in this work: StackExchange and TextWorld ACG. In contrast to previous keyphrase generation approaches, our model generates sets of diverse keyphrases of a variable number.


A Unified Framework for Pun Generation with Humor Principles

We propose a unified framework to generate both homophonic and homograph...

Keyphrase Generation with Correlation Constraints

In this paper, we study automatic keyphrase generation. Although convent...

A novel repetition normalized adversarial reward for headline generation

While reinforcement learning can effectively improve language generation...

Diverse and Specific Clarification Question Generation with Keywords

Product descriptions on e-commerce websites often suffer from missing im...

Diversifying Relevant Phrases

Diverse keyword suggestions for a given landing page or matching queries...

To Paraphrase or Not To Paraphrase: User-Controllable Selective Paraphrase Generation

In this article, we propose a paraphrase generation technique to keep th...

D-PAGE: Diverse Paraphrase Generation

In this paper, we investigate the diversity aspect of paraphrase generat...