ACL Findings2023
Echoes from Alexandria: A Large Resource for Multilingual Book Summarization
Alessandro Scirè, Simone Conia, Simone Ciciliano, Roberto Navigli
Abstract
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.
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Builds on5
- BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger et al.ICLR 2020 · 8,443 citations
- BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad et al.ACL 2020 · 1,224 citations
- BOOKSUM: A Collection of Datasets for Long-form Narrative SummarizationWojciech Kryscinski, Nazneen Rajani, Divyansh Agarwal, Caiming Xiong et al.EMNLP Findings 2022 · 89 citations
- Leveraging Lead Bias for Zero-shot Abstractive News SummarizationChenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng et al.SIGIR 2021 · 23 citations
- SummN: A Multi-Stage Summarization Framework for Long Input Dialogues and DocumentsYusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu et al.ACL 2022
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