--- title: "Who cites BIGPATENT: A Large-Scale — 63 citing papers" description: "63 papers of the Elamur Research library cite «BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization», found in their…" canonical: "https://research.elamur.ai/free/en/papers/sharma-2019-bigpatent-large-scale-dataset-abstractive/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/sharma-2019-bigpatent-large-scale-dataset-abstractive/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [BIGPATENT: A Large-Scale Dataset for Abstractive…](https://md.research.elamur.ai/free/en/papers/sharma-2019-bigpatent-large-scale-dataset-abstractive/) / cited by # Who cites «BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization» 63 papers of our library cite it (of the 141,840 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization»](https://md.research.elamur.ai/free/en/papers/sharma-2019-bigpatent-large-scale-dataset-abstractive/) 1. [**PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization arXiv:1912.08777**Jingqing Zhang, Yao Zhao, Mohammad Saleh, et al.*2019 · 1,816 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/) 2. **[Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Manzil Zaheer, Guru Guruganesh, Avinava Dubey, et al.*2020 · 1,002 citations* 3. **[Muppet: Massive Multi-task Representations with Pre-Finetuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 299 citations* 4. **[On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonathan Pilault, Raymond Li, Sandeep Subramanian, et al.*2020 · 260 citations* 5. **[TLDR: Extreme Summarization of Scientific Documents](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 260 citations* 6. [**Recursively Summarizing Books with Human Feedback arXiv:2109.10862**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/) 7. **[LooGLE: Can Long-Context Language Models Understand Long Contexts?](https://md.research.elamur.ai/free/en/papers/li-2023-loogle-long-context-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaqi Li, Mengmeng Wang, Zilong Zheng, et al.*2023 · 163 citations* 8. [**L-Eval: Instituting Standardized Evaluation for Long Context Language Models arXiv:2307.11088**Chenxin An, Shansan Gong, Ming Zhong, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/an-2023-l-eval-instituting-standardized-evaluation/) 9. **[Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://md.research.elamur.ai/free/en/papers/lu-2020-multi-xscience-large-scale-dataset/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yao Lu, Yue Dong, Laurent Charlin*2020 · 147 citations* 10. [**Nomic Embed: Training a Reproducible Long Context Text Embedder arXiv:2402.01613**Zach Nussbaum, John X. Morris, Brandon Duderstadt, et al.*2024 · 123 citations*](https://md.research.elamur.ai/free/en/papers/nussbaum-2024-nomic-embed-training-reproducible-long/) 11. **[MLSUM: The Multilingual Summarization Corpus](https://md.research.elamur.ai/free/en/papers/scialom-2020-mlsum-multilingual-summarization-corpus/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2020 · 114 citations* 12. [**MMTEB: Massive Multilingual Text Embedding Benchmark arXiv:2502.13595**Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/) 13. [**Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents arXiv:2310.19923**Michael Gunther, Jackmin Ong, Isabelle Mohr, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/gunther-2023-jina-embeddings-2-8192-token/) 14. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/) 49 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/sharma-2019-bigpatent-large-scale-dataset-abstractive/) — BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization