mlsanityData to 7 Oct 2026

Echoes from Alexandria: A Large Resource for Multilingual Book Summarization

arXiv 2306.04334 ↗First posted 7 Jun 2023cs.CLcited by 5

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Abstract (arXiv)

In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features. The task of full-book summarization presents additional challenges which are hard to tackle with current resources, due to their limited size and availability in English only. To overcome these limitations, we present "Echoes from Alexandria", or in shortened form, "Echoes", a large resource for multilingual book summarization. Echoes features three novel datasets: i) Echo-Wiki, for multilingual book summarization, ii) Echo-XSum, for extremely-compressive multilingual book summarization, and iii) Echo-FairySum, for extractive book summarization. To the best of our knowledge, Echoes, with its thousands of books and summaries, is the largest resource, and the first to be multilingual, featuring 5 languages and 25 language pairs. In addition to Echoes, we also introduce a new extractive-then-abstractive baseline, and, supported by our experimental results and manual analysis of the summaries generated, we argue that this baseline is more suitable for book summarization than purely-abstractive approaches. We release our resource and software at https://github.com/Babelscape/echoes-from-alexandria in the hope of fostering innovative research in multilingual book summarization.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 7 Jun 2023.

Builds on

15 of 30 references.

References · 15

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  2. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  3. 2020Longformer: The Long-Document Transformercited by 3,362
  4. 2018Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarizationcited by 1,308
  5. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  6. 2020SummEval: Re-evaluating Summarization Evaluationcited by 666
  7. 2017The NarrativeQA Reading Comprehension Challengecited by 590
  8. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
  9. 2021Recursively Summarizing Books with Human Feedbackcited by 287
  10. 2021LongT5: Efficient Text-To-Text Transformer for Long Sequencescited by 232
  11. 2021BookSum: A Collection of Datasets for Long-form Narrative Summarizationcited by 184
  12. 2018Bottom-Up Abstractive Summarizationcited by 177
3 more
  1. 2018Fast Abstractive Summarization with Reinforce-Selected Sentence Rewritingcited by 158
  2. 2021MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarizationcited by 104
  3. 2021Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documentscited by 64

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Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extractioncited by 28
  2. 2024RST-LoRA: A Discourse-Aware Low-Rank Adaptation for Long Document Abstractive Summarizationcited by 10
  3. 2024Knowledge-Centric Templatic Views of Documentscited by 5
  4. 2024Understanding Position Bias Effects on Fairness in Social Multi-Document Summarizationcited by 4
  5. 2025Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through Bookscited by 4