# Frustratingly Hard Evidence Retrieval for QA Over Books

arXiv: https://arxiv.org/abs/2007.09878 · HTML: https://mlsanity.com/paper/2007.09878/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 7 of 7)

- [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://mlsanity.com/paper/2401.18059/) (2024)
- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study](https://mlsanity.com/paper/2106.03826/) (2021)
- [ReadTwice: Reading Very Large Documents with Memories](https://mlsanity.com/paper/2105.04241/) (2021)
- [Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations](https://mlsanity.com/paper/2305.03117/) (2023)
- [SAGE: A Framework of Precise Retrieval for RAG](https://mlsanity.com/paper/2503.01713/) (2025)
- [Efficient Long Sequence Encoding via Synchronization](https://mlsanity.com/paper/2203.07644/) (2022)

## Abstract (arXiv)

A lot of progress has been made to improve question answering (QA) in recent years, but the special problem of QA over narrative book stories has not been explored in-depth. We formulate BookQA as an open-domain QA task given its similar dependency on evidence retrieval. We further investigate how state-of-the-art open-domain QA approaches can help BookQA. Besides achieving state-of-the-art on the NarrativeQA benchmark, our study also reveals the difficulty of evidence retrieval in books with a wealth of experiments and analysis - which necessitates future effort on novel solutions for evidence retrieval in BookQA.