Diversity in Ranking using Negative Reinforcement

TL;DR

This paper introduces a novel iterative method using negative reinforcement in Personalized PageRank to select diverse, central nodes in a graph, with applications in NLP summarization tasks.

Contribution

It proposes a new negative reinforcement-based approach to enhance diversity in graph ranking, improving upon existing methods.

Findings

Competitive performance on benchmark datasets

Effective in balancing centrality and diversity

Applicable to NLP summarization tasks

Abstract

In this paper, we consider the problem of diversity in ranking of the nodes in a graph. The task is to pick the top-k nodes in the graph which are both 'central' and 'diverse'. Many graph-based models of NLP like text summarization, opinion summarization involve the concept of diversity in generating the summaries. We develop a novel method which works in an iterative fashion based on random walks to achieve diversity. Specifically, we use negative reinforcement as a main tool to introduce diversity in the Personalized PageRank framework. Experiments on two benchmark datasets show that our algorithm is competitive to the existing methods.