Abstract. This paper describes the University of Houston team's efforts toward the problem of identifying reference spans in a reference document given sentences from other documents that cite the reference document. We investigated the following approaches: cosine similarity with multiple incremental modifications and SVMs with a tree kernel. Although the best performing approach in our experiments is quite simple, it is not the best under every metric used for comparison. We also present a brief analysis of the dataset which includes information on its sparsity and frequency of section titles.
REF used cosine similarity based on TFIDF weights for sentences with multiple incremental modifications and SVMs with a tree kernel.
University of Houston at CL-SciSumm 2016: SVMs with tree kernels and Sentence Similarity
BIRNDL@JCDL
Computer Science