Evaluation of sentence compression techniques against human performance

Abstract

This paper presents a comparison of various sentence compression techniques with human compressed sentences in the context of text summarization. Sentence compression is useful in text summarization as it allows to remove redundant and irrelevant information hence preserve space for more relevant information. In this paper, we evaluate recent state-of-the-art sentence compression techniques that are based on syntax alone, a mixture of relevancy and syntax, part of speech feature based machine learning, keywords alone and a naïve random word removal baseline. Results show that syntactic based techniques complemented by relevancy measures outperform all other techniques to preserve content in the task of text summarization. However, further analysis of human compressed sentences also shows that human compression techniques rely on world knowledge which is not captured by any automatic technique. © 2014 Springer-Verlag Berlin Heidelberg.