Newspaper Article Summarization by Neural Dodoitsu Generation.

Abstract

We describe a method for generating and singing a dodoitsu that summarizes a newspaper article. Specifically, we generate a training data set by modifying newspaper headlines to match the form of a dodoitsu, and train T5 to automatically generate a dodoitsu from an article. As a result, we were able to generate a dodoitsu for about 96% of the articles, and its quality as a news summary was about the same in ROUGE-1 precision and about half in recall compared to the headlines automatically generated by T5. In addition, by modifying DiffSinger, we succeeded in synthesizing a dodoitsu singing voice whose long tones are more stable than the original DiffSinger.