UIDS: A Multilingual Document Summarization Framework Based on Summary Diversity and Hierarchical Topics

Abstract

In this paper, we put forward UIDS, a new high-performing extensible framework for extractive MultiLingual Document Summarization. Our approach looks on a document in a multilingual corpus as an item sequence set, in which each sentence is an item sequence and each item is the minimal semantic unit. Then we formalize the extractive summary as summary diversity sampling problem that considers topic diversity and redundancy at the same time. The topic diversity is reflected using hierarchical topic models, the redundancy is reflected using similarity and the summary diversity is enhanced using Determinantal Point Processes. We then illustrate how this method encompasses a framework that is amenable to compute summaries for MultiLingual Single- and Multi-documents. Experiments on the MultiLing summarization task datasets demonstrate the effectiveness of our approach.