Coreference Resolution systems are typically evaluated on benchmarks containing small-to medium-scale documents.When it comes to evaluating long texts, however, existing benchmarks, such as LitBank, remain limited in length and do not adequately assess system capabilities at the book scale, i.e., when coreferring mentions span hundreds of thousands of tokens.To fill this gap, we first put forward a novel automatic pipeline that produces high-quality Coreference Resolution annotations on full narrative texts.Then, we adopt this pipeline to create the first book-scale coreference benchmark, BOOKCOREF, with an average document length of more than 200,000 tokens.We carry out a series of experiments showing the robustness of our automatic procedure and demonstrating the value of our resource, which enables current long-document coreference systems to gain up to +20 CoNLL-F1 points when evaluated on full books.Moreover, we report on the new challenges introduced by this unprecedented book-scale setting, highlighting that current models fail to deliver the same performance they achieve on smaller documents.We release our data and code to encourage research and development of new book-scale Coreference Resolution systems at https://github.com/sapienzanlp/bookcoref.

Question Answering (QA) on narrative text poses a unique challenge to current systems, requiring a deep understanding of long, complex documents.However, the reliability of NarrativeQA, the most widely used benchmark in this domain, is hindered by noisy documents and flawed QA pairs.In this work, we introduce LiteraryQA, a high-quality subset of Nar-rativeQA focused on literary works.Using a human-and LLM-validated pipeline, we identify and correct low-quality QA samples while removing extraneous text from source documents.We then carry out a meta-evaluation of automatic metrics to clarify how systems should be evaluated on LiteraryQA.This analysis reveals that all n-gram-based metrics have a low system-level correlation to human judgment, while LLM-as-a-Judge evaluations, even with small open-weight models, can strongly agree with the ranking identified by humans.Finally, we benchmark a set of long-context LLMs on LiteraryQA.We release our code and data at github.com/SapienzaNLP/LiteraryQA.