Sub-sentence Extraction Based on Combinatorial Optimization

Abstract

This paper describes the prospect of word extraction for text summarization based on combinatorial optimization. Instead of the commonly used sentence-based approach, word-based approaches are preferable if highly-compressed summarizations are required. However, naively applying conventional methods for word extraction yields excessively fragmented summaries. We avoid this by restricting the number of selected fragments from each sentence to at most one when formulating the maximum coverage problem. Consequently, the method only choose sub-sentences as fragments. Experiments show that our method matches the ROUGE scores of state-of-the-art systems without requiring any training or special parameters.