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Recently, we can obtain a practical abstractive document summarization model by fine-tuning a pre-trained language model (PLM). Since the pre-training for PLMs does not consider summarization-specific information such as the target summary length, there is a gap between the pre-training and fine-tuning for PLMs in summarization tasks. To fill the gap, we propose a method for enabling the model to understand the summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in the decoder in fine-tuning. Experimental results on the WikiHow, NYT, and CNN/DM datasets showed that our methods improve ROUGE scores from BART by generating summaries of appropriate lengths. Further, we observed about 3.0, 1,5, and 3.1 point improvements for ROUGE-1, -2, and -L, respectively, from GSum on the WikiHow dataset. Human evaluation results also showed that our methods improve the informativeness and conciseness of summaries., conference paper more...

Hierarchical text classification (HTC) aims to assign the most relevant labels with the hierarchical structure to an input text. However, handling unseen labels with considering a label hierarchy is still an open problem for real-world applications because traditional HTC models employ a pre-defined label set. To deal with this problem, we propose a generation-based classifier that leverages a Seq2Seq framework to capture a label hierarchy and unseen labels explicitly. Because of no available social media datasets that target at HTC, we constructed a new (Blog) dataset using pairs of social media posts and their hierarchical topic labels. Experimental results on the Blog dataset showed the effectiveness of our generation-based classifier over state-of-the-art baseline models. Human evaluation results showed that the quality of generated unseen labels outperforms even the gold labels., conference paper more...