Abstractive Summarization of Reddit Posts with Multi-level Memory Networks
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Byeongchang Kim, Hyunwoo Kim, Gunhee Kim. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
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Most text compression datasets are abstractive, treating prompt compression as a generative task where original prompts are rephrased into condensed ones.
“most text compression datasets are abstractive (Toutanova et al., 2016; Koupaee and Wang, 2018; Kim et al., 2019), meaning that they treat prompt compression as a generative task where the original prompts are rephrased into condensed ones.”
From LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (2024) · cites this paper as Kim et al., 2019
The indexed passage describing the Reddit TIFU dataset says it is highly abstractive, supporting Kim et al. as an example of an abstractive summarization dataset. It does not establish that most text-compression datasets are abstractive or discuss compressing prompts by rephrasing them. (LLM, conf=0.84)
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Cites
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911 · see citing works
Convolutional Sequence to Sequence Learning
Gehring, J., Auli, M., Grangier, D., Yarats, D., & Dauphin, Y. N. (2017) · arXiv · doi:10.48550/arxiv.1705.03122 · Cited by 2,850 · cited 2× · see citing works
“To validate the contribution of each component, we exclude one of key components from our model as follows: (i) -NoDilated with conventional convolutions instead, (ii) -NoMulti with no multi-level memory (iii) -NoNGTU with existing gated linear units (Gehring et al., 2017).”
MethodNarayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1206 · Cited by 1,207 · see citing works
Regularizing Neural Networks by Penalizing Confident Output Distributions
Pereyra, G., Tucker, G., Chorowski, J., Kaiser, Ł., & Hinton, G. (2017) · arXiv · doi:10.48550/arxiv.1701.06548 · Cited by 655 · see citing works
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Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus
Dodge, J., Sap, M., Marasović, A., Agnew, W., Ilharco, G., Groeneveld, D., Mitchell, M., & Gardner, M. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.98 · Cited by 306 · see citing works
GSum: A General Framework for Guided Neural Abstractive Summarization
Dou, Z.-Y., Liu, P., Hayashi, H., Jiang, Z., & Neubig, G. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.384 · Cited by 210 · see citing works
Few-Shot Text Generation with Natural Language Instructions
Schick, T., & Schütze, H. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.32 · Cited by 107 · see citing works
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Pan, Z., Wu, Q., Jiang, H., Xia, M., Luo, X., Zhang, J., Lin, Q., Rühle, V., Yang, Y., Lin, C.-Y., et al. (2024) · Findings of the Association for Computational Linguistics: ACL 2024 · doi:10.18653/v1/2024.findings-acl.57 · Cited by 58 · see citing works
Make The Most of Prior Data: A Solution for Interactive Text Summarization with Preference Feedback
Nguyen, D.-H., Nghiem, N. V. D., Nguyen, B.-S., Tien Le, D. T., Sabahi, S., Nguyen, M.-T., & Le, H. (2022) · Findings of the Association for Computational Linguistics: NAACL 2022 · doi:10.18653/v1/2022.findings-naacl.147 · Cited by 6 · see citing works
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression
Pan, Z., Wu, Q., Jiang, H., Xia, M., Luo, X., Zhang, J., Lin, Q., Rühle, V., Yang, Y., Lin, C.-Y., et al. (2024) · arXiv · Cited by 2 · see citing works
“For Q1, most text compression datasets are ab-stractive (Toutanova et al., 2016; Koupaee and Wang, 2018; Kim et al., 2019), meaning that they treat prompt compression as a generative task where the original prompts are rephrased into condensed ones.”
Background
- Authors
- Kim, B., Kim, H., & Kim, G.
- Venue
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Published
- 2019
- Pages
- 2519-2531
- OpenAlex
- W2962788840
- Cited by
- 96
- References
- 5
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 7 · 2023: 13 · 2022: 14 · 2021: 28 · 2020: 19 · 2019: 10
- Type
- conference abstract
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- abstractive summarization, Reddit posts, text summarization, social media posts
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