  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.
