Extractive Summarization as Text Matching
Table 4: Results on test sets of Reddit and XSum. Num indicates how many sentences BertExt extracts as a summary and Sel indicates the number of sentences we choose to form a candidate summary.
['[BOLD] Model', '[BOLD] R-1', '[BOLD] R-2', '[BOLD] R-L']
When taking just one sentence to match the original document, MatchSum degenerates into a re-ranking of sentences. However, when the number of sentences increases to two and summary-level semantics need to be taken into account, MatchSum can obtain a more remarkable improvement (compared to BertExt (Num = 2), 1.04 ΔR-1 on Reddit, 1.62 ΔR-1 on XSum). In addition, our model maps candidate summary as a whole into semantic space, so it can flexibly choose any number of sentences, while most other methods can only extract a fixed number of sentences.