Diversity in Ranking using Negative Reinforcement
This addresses diversity in ranking for graph-based NLP tasks like text summarization, but it is incremental as it builds on existing frameworks.
The paper tackles the problem of selecting top-k nodes in a graph that are both central and diverse, using a novel method based on negative reinforcement in the Personalized PageRank framework. Experiments on two benchmark datasets show the algorithm is competitive with existing methods.
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.