SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648

Interrater reliability: The kappa statistic

McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877

Untitled work

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Get To The Point: Summarization with Pointer-Generator Networks

See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775

A Neural Attention Model for Abstractive Sentence Summarization

Rush, A. M., Chopra, S., & Weston, J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1044 · Cited by 2,932

Teaching Machines to Read and ComprehendPreprint

Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855

fairseq: A Fast, Extensible Toolkit for Sequence Modeling

Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-4009 · Cited by 2,475

Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., & Xiang, B. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1028 · Cited by 2,361

Abstractive Sentence Summarization with Attentive Recurrent Neural Networks

Chopra, S., Auli, M., & Rush, A. M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1012 · Cited by 896

MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization

Feng, X., Feng, X., & Qin, B. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.1 · Cited by 16

Analyzing and Evaluating Faithfulness in Dialogue Summarization

Wang, B., Zhang, C., Zhang, Y., Chen, Y., & Li, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.325 · Cited by 25

Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches, and Future Directions

Jia, Q., Liu, Y., Ren, S., & Zhu, K. Q. (2023) · ACM Computing Surveys · doi:10.1145/3622933 · Cited by 20

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Liang, X., Wu, S., Cui, C., Bai, J., Bian, C., & Li, Z. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.2 · Cited by 17

A Comparative Study of PEGASUS, BART, and T5 for Text Summarization Across Diverse Datasets

Daraghmi, E. Y., Atwe, L., & Jaber, A. (2025) · Future Internet · doi:10.3390/fi17090389 · Cited by 17

Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly

Woisetschläger, H., Erben, A., Wang, S., Mayer, R., & Jacobsen, H. (2024) · Workshop on Data Management for End-to-End Machine Learning (DEEM) · doi:10.1145/3650203.3663331 · Cited by 32

Data Contamination Quiz: A Tool to Detect and Estimate Contamination in Large Language Models

Golchin, S., & Surdeanu, M. (2025) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl.a.20 · Cited by 85

CLTS: A New Chinese Long Text Summarization Dataset

Liu, X., Zhang, C., Chen, X., Cao, Y., & Li, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60450-9_42 · Cited by 15

DialogSum Challenge: Summarizing Real-Life Scenario Dialogues

Chen, Y., Liu, Y., & Zhang, Y. (2021) · International Conference on Natural Language Generation · doi:10.18653/v1/2021.inlg-1.33 · Cited by 14

FLEdge: Benchmarking Federated Learning Applications in Edge Computing Systems

Woisetschläger, H., Erben, A., Mayer, R., Wang, S., & Jacobsen, H. (2024) · International Middleware Conference · doi:10.1145/3652892.3700751 · Cited by 14

On the hidden treasure of dialog in video question answering

Engin, D., Schnitzler, F., Duong, N. Q. K., & Avrithis, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00207 · Cited by 15

GupShup: Summarizing Open-Domain Code-Switched Conversations

Mehnaz, L., Mahata, D., Gosangi, R., Gunturi, U. S., Jain, R., Gupta, G., Kumar, A., Lee, I. G., Acharya, A., & Shah, R. R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.499 · Cited by 17

Record Once, Post Everywhere: Automatic Shortening of Audio Stories for Social Media

Wang, B. K., Jin, Z., & Mysore, G. J. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545680 · Cited by 18

Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval

Jung, M., Choi, S., Kim, J. C., Kim, J.-H., & Zhang, B. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.530 · Cited by 15

Evidence, my Dear Watson: Abstractive dialogue summarization on learnable relevant utterances

Italiani, P., Frisoni, G., Moro, G., Carbonaro, A., & Sartori, C. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127132 · Cited by 13

Derivative-Free Optimization for Low-Rank Adaptation in Large Language Models

Jin, F., Liu, Y., & Tan, Y. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3477330 · Cited by 27

Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization

Zhao, L., Zheng, F., Zeng, W., He, K., Xu, W., Jiang, H., Wu, W., & Wu, Y. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.357 · Cited by 13

AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models

Kwon, S. J., Kim, J., Bae, J., Yoo, K. M., Kim, J.-H., Park, B., Kim, B., Ha, J.-W., Sung, N., & Lee, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.240 · Cited by 17

UniDU: Towards A Unified Generative Dialogue Understanding Framework

Chen, Z., Chen, L., Chen, B., Qin, L., Liu, Y., Su, Z., Lou, J., & Yu, K. (2022) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2022.sigdial-1.43 · Cited by 14

Learn to Copy from the Copying History: Correlational Copy Network for Abstractive Summarization

Li, H., Xu, S., Yuan, P., Wang, Y., Wu, Y., He, X., & Zhou, B. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.336 · Cited by 12

Few-shot fine-tuning SOTA summarization models for medical dialogues

Navarro, D. F., Dras, M., & Berkovsky, S. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.32 · Cited by 12

Topic Shift Detection for Mixed Initiative Response

Konigari, R., Ramola, S., Alluri, V. V., & Shrivastava, M. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.17 · Cited by 13

Data Augmentation for Low-Resource Dialogue Summarization

Liu, Y., Maynez, J., Simões, G., & Narayan, S. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.53 · Cited by 13

Improving Automatic Summarization for Browsing Longform Spoken Dialog

Li, D., Chen, T., Zadikian, A., Tung, A., & Chilton, L. B. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581339 · Cited by 13

MLASK: Multimodal Summarization of Video-based News Articles

Krubiński, M., & Pecina, P. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.67 · Cited by 14

Evidential Reasoning and Learning: a Survey

Cerutti, F., Kaplan, L., & Şensoy, M. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/760 · Cited by 13

Text summarization for pharmaceutical sciences using hierarchical clustering with a weighted evaluation methodology

Dalal, A. J., Ranjan, S., Bopaiah, Y., Chembachere, D., Steiger, N., Burns, C., & Daswani, V. (2024) · Scientific Reports · doi:10.1038/s41598-024-70618-w · Cited by 14

Fine tuning the large language pegasus model for dialogue summarization

Sarthak, Rishiwal, V., Yadav, P., Yadav, M., Gangwar, S., & Shankdhar, A. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-02307-w · Cited by 11

GPT Models Can Perform Thematic Analysis in Public Health Studies, Akin to Qualitative Researchers

Yang, Y., Alba, C., Wang, C., Wang, X., Anderson, J., & An, R. (2024) · Journal of Social Computing · doi:10.23919/jsc.2024.0024 · Cited by 14

A Finer-grain Universal Dialogue Semantic Structures based Model For Abstractive Dialogue Summarization

Lei, Y., Zheng, F., Yan, Y., He, K., & Xu, W. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.117 · Cited by 11

VCSUM: A Versatile Chinese Meeting Summarization Dataset

Wu, H., Zhan, M., Tan, H., Hou, Z., Ding, L., & Song, L. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.377 · Cited by 21

ForumSum: A Multi-Speaker Conversation Summarization Dataset

Khalman, M., Zhao, Y., & Saleh, M. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.391 · Cited by 11

Deep Learning for Natural Language Processing

Wang, Y., Li, Z., Deng, Z., Song, H., & Yang, J. (2023) · Artificial intelligence · doi:10.5772/intechopen.112550 · Cited by 10

FLAME: Fully Leveraging MoE Sparsity for Transformer on FPGA

Lin, X., Tian, H., Xue, W., Ma, L., Cao, J., Zhang, M., Yu, J. W., & Wang, K. (2024) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3649329.3656507 · Cited by 10

Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization

Zhao, L., Zeng, W., Xu, W., & Guo, J. H. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.209 · Cited by 10

Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization

Li, C., Wang, L., Lin, X. A., de Melo, G., & He, L. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.72 · Cited by 10

Towards Summary Candidates Fusion

Ravaut, M., Joty, S., & Chen, N. F. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.581 · Cited by 12

Dialogue summarization with supporting utterance flow modelling and fact regularization

Chen, W., Li, P., Chan, H. P., & King, I. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107328 · Cited by 10

Discourse-Aware Soft Prompting for Text Generation

Ghazvininejad, M., Karpukhin, V., Gor, V., & Çelikyılmaz, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.303 · Cited by 12

Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling

Li, Y., Zhao, H. Y., & Zhang, Z. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.177 · Cited by 11

Language Model as an Annotator: Exploring DialoGPT for Dialogue SummarizationPreprint

Feng, X., Feng, X., Qin, L., Qin, B., & Liu, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.12544 · Cited by 10

Feature-Based Federated Transfer Learning: Communication Efficiency, Robustness and Privacy

Wang, F., Gursoy, M. C., & Velipasalar, S. (2024) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2024.3408131 · Cited by 10

Evaluating Multilingual Abstractive Dialogue Summarization in Indian Languages using mT5-small & IndicBART

Sharma, M., Goyal, G., Gupta, A., Rani, R., Sharma, A. K., & Dev, A. (2024) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct61223.2024.10543588 · Cited by 9

Revolutionizing textual data insights: A comprehensive review of the dual relationship between transformers and clustering in textual data analysis

Aghayengejeh, N. P., Balafar, M. A., & Nikzad-Khasmakhi, N. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100792 · Cited by 10

Post-Training Dialogue Summarization using Pseudo-Paraphrasing

Jia, Q., Liu, Y., Tang, H., & Zhu, K. Q. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.125 · Cited by 9

Unsupervised Abstractive Dialogue Summarization with Word Graphs and POV Conversion

Park, S., & Lee, J. (2022) · doi:10.18653/v1/2022.wit-1.1 · Cited by 9

An End-to-End Dialogue Summarization System for Sales Calls

Asi, A., Wang, S., Eisenstadt, R., Geckt, D., Kuper, Y., Mao, Y., & Ronen, R. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-industry.6 · Cited by 14

Learning Disentangled Representation via Domain Adaptation for Dialogue Summarization

Li, J., Xia, Y., Cheng, X., Zhao, D., & Yan, R. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583389 · Cited by 10

Generating EDU Extracts for Plan-Guided Summary Re-Ranking

Adams, G., Fabbri, A. R., Ladhak, F., Elhadad, N., & McKeown, K. R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.151 · Cited by 10

Action-Item-Driven Summarization of Long Meeting Transcripts

Golia, L., & Kalita, J. K. (2023) · International Conference on Natural Language Processing and Information Retrieval (NLPIR) · doi:10.1145/3639233.3639253 · Cited by 17

Let Topic Flow: A Unified Topic-Guided Segment-Wise Dialogue Summarization Framework

Han, Q., Yang, Z., Lin, H., & Qin, T. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3374112 · Cited by 9

Fine-Tuning Large Language Models for Digital Forensics: Case Study and General Recommendations

Michelet, G., Henseler, H., van Beek, H., Scanlon, M., & Breitinger, F. (2025) · Digital Threats Research and Practice · doi:10.1145/3748264 · Cited by 9

Learning to Imagine: Visually-Augmented Natural Language Generation

Tang, T., Chen, Y., Du, Y., Li, J., Zhao, W. X., & Wen, J.-R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.526 · Cited by 12

NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization

Zhao, C., Brahman, F., Song, K., Yao, W., Yu, D., & Chaturvedi, S. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.14 · Cited by 13

Improving Abstractive Dialogue Summarization Using Keyword Extraction

Yoo, C., & Lee, H. (2023) · Applied Sciences · doi:10.3390/app13179771 · Cited by 9

SummerTime: Text Summarization Toolkit for Non-experts

Ni, A., Azerbayev, Z., Mutuma, M., Feng, T., Zhang, Y., Tao, Y., Awadallah, A. H., & Radev, D. (2021) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2021.emnlp-demo.37 · Cited by 9

Who Speaks Like a Style of Vitamin: Towards Syntax-Aware Dialogue Summarization Using Multi-Task Learning

Lee, S., Yang, K., Park, C., Sedoc, J., & Lim, H. (2021) · IEEE Access · doi:10.1109/access.2021.3124556 · Cited by 9

SPEC: Summary Preference Decomposition for Low-Resource Abstractive Summarization

Chen, Y.-S., Song, Y.-Z., & Shuai, H.-H. (2022) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2022.3230539 · Cited by 8

Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond

Chang, H.-S., Yao, Z., Gon, A., Yu, H. Z., & McCallum, A. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.805 · Cited by 8

Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts

Ye, Q., Zha, J., & Ren, X. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.189 · Cited by 19

Instructive Dialogue Summarization with Query Aggregations

Wang, B., Liu, Z., & Chen, N. F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.474 · Cited by 10

A Novel Topic Segmentation Approach for Enhanced Dialogue Summarization

Ren, Z. (2024) · Journal of improved oil and gas recovery technology. · doi:10.53469/wjimt.2024.07(04).05 · Cited by 8

Simulated Chats for Task-oriented Dialog: Learning to Generate Conversations from Instructions.Preprint

Mohapatra, B., Pandey, G. S., Contractor, D., & Joshi, S. (2020) · arXiv (Cornell University) · Cited by 9

GupShup: An Annotated Corpus for Abstractive Summarization of Open-Domain Code-Switched ConversationsPreprint

Mehnaz, L., Mahata, D., Gosangi, R., Gunturi, U. S., Jain, R., Gupta, G., Kumar, A., Lee, I. G., Acharya, A., & Shah, R. R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.08578 · Cited by 9

Enhancing Clinical Note Generation from Doctor-Patient Conversations through Semantic Partition-Oriented Summarization

Nguyen, B.-N., Le, H.-Q., & Can, D.-C. (2023) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse59128.2023.10299512 · Cited by 7

Adaptation Approaches for Nearest Neighbor Language Models

Bhardwaj, R., Polovets, G., & Sunkara, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.73 · Cited by 7

InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT

Yi‐chong, X., Xu, R., Iter, D., Liu, Y., Wang, S., Zhu, C., & Zeng, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.927 · Cited by 12

MoIL: Momentum Imitation Learning for Efficient Vision-Language Adaptation

Luo, G., Zhou, Y., Huang, M., Ren, T., Sun, X., & Ji, R. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3435790 · Cited by 10

Integrating Stickers into Multimodal Dialogue Summarization: A Novel Dataset and Approach for Enhancing Social Media Interaction

Shi, Y., & Kong, F. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3680978 · Cited by 7

Leveraging speaker-aware structure and factual knowledge for faithful dialogue summarization

Zhao, L., Xu, W., Zhang, C., & Guo, J. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108550 · Cited by 7

Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets

Tuggener, D., Mieskes, M., Deriu, J., & Cieliebak, M. (2021) · Workshop on New Frontiers in Summarization (NewSum) · doi:10.18653/v1/2021.newsum-1.12 · Cited by 7

FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning

Ye, Q., Beltagy, I., Peters, M. E., Ren, X., & Hajishirzi, H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.454 · Cited by 8

SWING: Balancing Coverage and Faithfulness for Dialogue Summarization

Huang, K.-H., Singh, S., Ma, X., Xiao, W., Nan, F., Dingwall, N., Wang, W. Y., & McKeown, K. R. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.37 · Cited by 12

Towards Better Evaluation of Instruction-Following: A Case-Study in Summarization

Skopek, O., Aralikatte, R., Gooding, S., & Cărbune, V. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.16 · Cited by 17

Team Hitachi @ AutoMin 2021: Reference-free Automatic Minuting Pipeline with Argument Structure Construction over Topic-based Summarization

Yamaguchi, A., Morio, G., Ozaki, H., Yokote, K.-I., & Nagamatsu, K. (2021) · doi:10.21437/automin.2021-4 · Cited by 8

Interactive User Interface for Dialogue Summarization

Jung, J., Seo, H., Jung, S., Chung, R., Ryu, H., & Chang, D.-S. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584057 · Cited by 6

Generic Dependency Modeling for Multi-Party Conversation

Shen, W., Quan, X., & Yang, K. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096003 · Cited by 6

NonFactS: NonFactual Summary Generation for Factuality Evaluation in Document Summarization

Soleimani, A., Monz, C., & Worring, M. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.400 · Cited by 7

Can you Summarize my learnings? Towards Perspective-based Educational Dialogue Summarization

Jain, R., Saha, T., Lalwani, J., & Saha, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.208 · Cited by 6

mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images

Overbay, K., Ahn, J., Zadeh, F. P., Park, J., & Kim, G. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.251 · Cited by 8

Exploring the Impact of Model Scaling on Parameter-Efficient Tuning

Su, Y., Chan, C.-M., Cheng, J., Qin, Y., Lin, Y., Hu, S., Yang, Z., Ding, N., Sun, X., Xie, G., et al. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.931 · Cited by 8

PORTRAIT: A Hybrid Approach to Create Extractive Ground-truth Summary for Disaster Event

Garg, P. K., Chakraborty, R., & Dandapat, S. K. (2025) · ACM Transactions on the Web · doi:10.1145/3711908 · Cited by 6

SumTitles: a Summarization Dataset with Low Extractiveness

Malykh, V., Chernis, K., Artemova, E., & Piontkovskaya, I. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.503 · Cited by 6

Human-in-the-loop Abstractive Dialogue Summarization

Chen, J., Dodda, M., & Yang, D. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.584 · Cited by 12

Joint Generator-Ranker Learning for Natural Language Generation

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