MatInf: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
Table 8: Experimental results of baseline methods on CNN / DM cnndm, LCSTS lcsts, and MatInf-Summ.
['Method', 'CNN/DM R-1', 'CNN/DM R-2', 'CNN/DM R-L', 'LCSTS R-1', 'LCSTS R-2', 'LCSTS R-L', 'MatInf-Summ R-1', 'MatInf-Summ R-2', 'MatInf-Summ R-L']
Summarization. By comparing the performance of two basic baselines, TextRank textrank and Seq2Seq+Att s2satt, we can see an obvious difference in performance between extractive and abstractive methods on datasets of different genres. BertAbs bertabs, the powerful BERT-based model, significantly outperforms all other baselines on MatInf-Summ thanks to its exploitation of pretraining and the capacity of a BERT model. For MTF-S2S, it outperforms the single-task counterpart by 4.73 on ROUGE-L.