Jointly Extracting and Compressing Documentswith Summary State Representations
Table 1: Oracle scores obtained for the CNN and DailyMail testsets. We report ROUGE-1 (R1), ROUGE-2 (R2) and ROUGE-L (RL) F1 scores.
['Oracle', 'R1', 'R2', 'RL']
Our compressive oracle achieves much better scores than the extractive oracle, because of its capability to make summaries concise. Moreover, the linguistic quality of these oracles was preserved due to the tagging of the entire span by the sentence compressor trained on the sentence compression dataset. We believe that our dataset with oracle compression labels will be of significant interest to the sentence compression and summarization community. After tagging all sentences in the CNN and DailyMail corpora using this compression model, we generated oracle compressive summaries based on the best average of ROUGE-1 and ROUGE-2 F1 scores from the combination of all possible sentences with all combinations of removals of the marked compression chunks. To solve this combinatorial problem, our algorithm recursively selects the n possible sentences with the best accumulated score. We only allow a maximum of seven compressed sentences per oracle. Our compressive oracle achieves much better scores than the extractive oracle because of its capability to make summaries concise (see Moreover, the linguistic quality of these oracles was preserved due to the tagging of the entire span by the sentence compressor.