GASP!Generating Abstracts of Scientific Papers from Abstracts of Cited Papers
Table 2: Rouge performance of TextRank and BiLSTM on the testing of the GASP Corpus
['[EMPTY]', '[ITALIC] R2 Recall', 'R2 Precision', 'R2 F1', 'R1 Recall', 'R1 Precision', 'R1 F1', 'RL Recall', 'RL Precision', 'RL F1']
In general, the extractive text summarization algorithm TextRank tends to have and higher recall and a lower precision respect to the other system This happens because the TextRank algorithm reuse words ans sentences in source abstracts to match the fixed 50 words length of the summary. In contrast, the abstractive BiLSTM system tends to be more precise losing points in recall. However, some BiLSTM outpus are really extremely odd. For example, mostly for medical paper, the BiLSTM system performs poorly producing short texts like: Clinical pulmonary a a a a a a a a a a. This fact is due probably to topic distribution of papers in the training set that could be overcome with an extended version of the GASP corpus.