  The majority of available text summarization datasets include short-form
source documents that lack long-range causal and temporal dependencies, and
often contain strong layout and stylistic biases. While relevant, such datasets
will offer limited challenges for future generations of text summarization
systems. We address these issues by introducing BookSum, a collection of
datasets for long-form narrative summarization. Our dataset covers source
documents from the literature domain, such as novels, plays and stories, and
includes highly abstractive, human written summaries on three levels of
granularity of increasing difficulty: paragraph-, chapter-, and book-level. The
domain and structure of our dataset poses a unique set of challenges for
summarization systems, which include: processing very long documents,
non-trivial causal and temporal dependencies, and rich discourse structures. To
facilitate future work, we trained and evaluated multiple extractive and
abstractive summarization models as baselines for our dataset.
