Paper: Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach

ACL ID D14-1170
Title Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this paper, we investigate a challeng- ing task of automatic related work gener- ation. Given multiple reference papers as input, the task aims to generate a related work section for a target paper. The gen- erated related work section can be used as a draft for the author to complete his or her final related work section. We propose our Automatic Related Work Generation system called ARWG to ad- dress this task. It first exploits a PLSA model to split the sentence set of the giv- en papers into different topic-biased parts, and then applies regression models to learn the importance of the sentences. At last it employs an optimization frame- work to generate the related work section. Our evaluation results on a test set of 150 target papers along with their reference papers...