Papers with NarrativeQA
Book QA: Stories of Challenges and Opportunities (D19-58)
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| Challenge: | Existing approaches to answer questions based on the full text of books are limited by their unique characteristics. |
| Approach: | They propose a system for answering questions based on the full text of books . they use a memory network to reason and predict an answer, and a novel question generator to improve generalization. |
| Outcome: | The proposed system improves on the recently published NarrativeQA corpus on Who questions . it shows that the proposed system is highly challenging and needs more research . |
Multi-style Generative Reading Comprehension (P19-1)
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Kyosuke Nishida, Itsumi Saito, Kosuke Nishida, Kazutoshi Shinoda, Atsushi Otsuka, Hisako Asano, Junji Tomita
| Challenge: | Current studies on generative reading comprehension (RC) focus on extracting an answer span from textual evidence and natural language generation (NLG). |
| Approach: | They propose a multi-style abstractive summarization model for question answering called Masque. |
| Outcome: | The proposed model achieves state-of-the-art performance on the Q&A and Q& A + NLG tasks of MS MARCO and NarrativeQA. |
Self-Taught Agentic Long Context Understanding (2025.acl-long)
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Yufan Zhuang, Xiaodong Yu, Jialian Wu, Ximeng Sun, Ze Wang, Jiang Liu, Yusheng Su, Jingbo Shang, Zicheng Liu, Emad Barsoum
| Challenge: | Extensive experiments across seven long-context tasks demonstrate that AgenticLU significantly outperforms state-of-the-art prompting methods and specialized long-consumer LLMs. |
| Approach: | They propose a framework to enhance an LLM's understanding of long-context questions by integrating targeted self-clarification with contextual grounding within an agentic workflow. |
| Outcome: | The proposed framework outperforms state-of-the-art prompting methods and specialized long-context LLMs in seven long-constitut tasks. |
Commonsense for Generative Multi-Hop Question Answering Tasks (D18-1)
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| Challenge: | Reading comprehension QA tasks have seen a recent surge in popularity, yet most work has focused on fact-finding extractive QA. |
| Approach: | They propose a multi-hop generative task that uses a pointer-generator decoder to synthesize disjoint pieces of information within the context to generate an answer. |
| Outcome: | The proposed model performs better than previous generative models and is competitive with current state-of-the-art span prediction models. |
A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information (2023.findings-acl)
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| Challenge: | Existing question answering methods assume that the input content can always be accessed while answering the question. |
| Approach: | They propose a model that performs rehearsal and anticipation while processing inputs to memorize important information for question answering tasks from streaming data. |
| Outcome: | The proposed model improves on short-sequence (bAbI) and large-squence textual (NarrativeQA) and video (ActivityNet-QA) question answering datasets. |
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking (2023.findings-acl)
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| Challenge: | Existing methods for annotating long-document question answering are based on short documents and can hardly incorporate long-range information. |
| Approach: | They propose an unsupervised method to generate long-document question answering pairs . they propose a method to aggregate and generate answers with long-range dependency . |
| Outcome: | The proposed method outperforms existing methods on NarrativeQA and Qasper. |
Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for storing and retrieving memory are limited by shallow semantic retrieval. |
| Approach: | They propose a memory mechanism that organizes and retrieves past experiences to support decision-making. |
| Outcome: | Experiments on LoCoMo and NarrativeQA show that CompassMem improves retrieval and reasoning performance across multiple backbone models. |
LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA (2025.emnlp-main)
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| Challenge: | Existing Question Answering systems are limited by noisy documents and flawed QA pairs. |
| Approach: | They propose a high-quality subset of NarrativeQA focused on literary works . they identify and correct low-quality QA samples while removing extraneous text . |
| Outcome: | The proposed subset of NarrativeQA is based on literary works. |