Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
This provides a large-scale, high-quality dataset for summarization research, but it is incremental as it builds on existing data collection efforts.
The authors introduced NEWSROOM, a dataset of 1.3 million news articles and summaries from 38 publications, spanning 1998 to 2017, to address the need for diverse summarization data, and they analyzed its strategies and trained existing methods to assess its utility.
We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications. Extracted from search and social media metadata between 1998 and 2017, these high-quality summaries demonstrate high diversity of summarization styles. In particular, the summaries combine abstractive and extractive strategies, borrowing words and phrases from articles at varying rates. We analyze the extraction strategies used in NEWSROOM summaries against other datasets to quantify the diversity and difficulty of our new data, and train existing methods on the data to evaluate its utility and challenges.