Exploring Domain Shift in Extractive Text Summarization
Table 4: Comparison between our strategies with other extractive summarization models on non-anonymized CNN/Daily Mail provided by See2017. The red up-arrows indicate performance improvement over our base model, and the green down-arrows denote the degradation.
['Model', 'R-1', 'R-2', 'R-L']
Inspired by such observations, we further employ our four learning strategies to the mainstream summarization dataset CNN/DailyMail See2017, which also includes two different data sources: CNN and DailyMail. We use the publication as the domain and train our models on its 28w training set. Besides, the publication tags can improve ROUGE scores significantly by 0.13 points in ROUGE-1 and the meta learning strategy does not show many advantages when dealing with in-domain examples, what we have expected. BERT with tags achieves the best performance, although the performance increment is not as much as what publication tags bring to the basic model, which we suppose that BERT itself has contained some degree of domain information.