Single-Document Summarization as a Tree Knapsack Problem
Tsutomu Hirao, Yasuhisa Yoshida, Masaaki Nishino, Norihito Yasuda, Masaaki Nagata · 2013
Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem, a Maximum Coverage Problem or a Budgeted Median Problem.These methods successfully improved summarization quality, but they did not consider the rhetorical relations between the textual units of a source document.Thus, summaries generated by these methods may lack logical coherence.This paper proposes a single document summarization method based on the trimming of a discourse tree.This is a two-fold process.First, we propose rules for transforming a rhetorical structure theorybased discourse tree into a dependency-based discourse tree, which allows us to take a treetrimming approach to summarization.Second, we formulate the problem of trimming a dependency-based discourse tree as a Tree Knapsack Problem, then solve it with integer linear programming (ILP).Evaluation results showed that our method improved ROUGE scores.