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 9: Results for identifying the discourse facets for the retrieved contexts. The metrics are Precision (P), Recall (R), and F1-score (F) of the identified discourse facets contingent on the correct retrieved span.
['Method', 'P', 'R', 'F']
We do not report the results of low performing systems. However, since only the correct retrieved contexts are rewarded, the performance of each model differs based on the accuracy of retrieving the correct contexts. We observe that most of our methods (except for the QR-NP) improve over all the baselines in terms of all metrics. We obtain substantial improvements especially in terms of precision. The best method for identifying the discourse facets is the supervised method (indicated with “supervised” in the Table) which obtains 36.1% F-1 score, improving the best baseline (“Jaccard Focused Method”) by 16%. Embedding methods also perform well by obtaining F-1 scores of 33.1% for the Wikipedia embeddings, and 34.8% for the retrofitted embeddings. These results further show the effectiveness of our contextualization methods along with the proposed classifier for identifying the facets.