In this paper, we investigate a challenging task of automatic related work generation. Given multiple reference papers as input, the task aims to generate a related work section for a target paper. The generated 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 address this task. It first exploits a PLSA model to split the sentence set of the given papers into different topic-biased parts, and then applies regression models to learn the importance of the sentences. At last it employs an optimization framework to generate the related work section. Our evaluation results on a test set of 150 target papers along with their reference papers show that our proposed ARWG system can generate related work sections with better quality. A user study is also performed to show ARWG can achieve an improvement over generic multi-document summarization baselines.
Subsequently, REF exploit probabilistic latent semantic indexing to split candidate texts into different topic-biased parts, then apply several regression models to learn the importance of each sentence.
Automatic Generation of Related Work Sections in Scientific Papers: An Optimization Approach
EMNLP
Computer Science Engineering