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 10: The classifier’s intrinsic performance for identifying the discourse facets on the CL-SciSum dataset.
['Discourse Facet', 'P', 'R', 'F', '#']
As illustrated, the weighed average F1 performance over all discourse facets is 0.73. One challenge in identifying the discourse facets is the unbalanced dataset and the limited number of training examples for some specific facets. As also reflected in the table, we observe that for categories with smaller number of instances, the performance is generally lower. We therefore believe that having more training samples in the rare categories could further increase the performance.