Scientific document summarization via citation contextualization and scientific discourse {}^{*} This is a pre-print of an article published on IJDL. The final publication is available at Springer via http://dx.doi.org/10.1007/s00799-017-0216-8
Table 14: The effect of discourse facets on the summarization results on the TAC and CL-SciSum dataset based on QR-NP approach by greedy sentence selection strategy on the identified facets. Other approaches show similar positive trends. Metrics are Rouge F-scores.
['[EMPTY]', 'R-2', 'R-3', 'R-SU4']
To better analyze the effect of identifying discourse facets on the overall quality of the summary, we compare the Rouge scores of the summary generated by our approach with and without this step. We observe that grouping citation contexts by their corresponding discourse facet has a positive effect on the quality of the summary on both datasets (17% and 55% improvements over TAC and CL-SciSum datasets in terms of Rouge-2, respectively). This is because identifying facets and grouping the contextualized citations by facets, results in a summary that captures the content from all sections of the paper. We observe similar trends for other variants of our approaches; for brevity we only show the results for QR-NP as an illustrative analysis on the effect of identifying discourse facets on the quality of the generated summary.