The CL-SciSumm 2016 shared task introduced an interesting problem: given a document D and a piece of text that cites D, how do we identify the text spans of D being referenced by the piece of text? The shared task provided the first annotated dataset for studying this problem. We present an analysis of our continued work in improving our system's performance on this task. We demonstrate how topic models and word embeddings can be used to surpass the previously best performing system.
@article{arxiv.1708.02989,
title = {Identifying Reference Spans: Topic Modeling and Word Embeddings help IR},
author = {Luis Moraes and Shahryar Baki and Rakesh Verma and Daniel Lee},
journal= {arXiv preprint arXiv:1708.02989},
year = {2017}
}