The 4th New Frontiers in Summarization Workshop

EMNLP 2023

The Fourth Workshop on “New Frontiers in Summarization” aims to promote the cross-fertilization of ideas in automatic summarization and related fields. This includes discussion on novel paradigms, shared tasks of interest, applied research and applications, and possible future research directions. In addition to building a cohesive research community, the workshop will accelerate knowledge diffusion by developing new tools, datasets, and resources that are in line with the summarization needs of academia, industry, and government.

New advances in natural language processing (e.g., pre-trained models and prompt-based learning) have resulted in state-of-the-art performance according to existing standards of summarization evaluation. A number of new challenges have emerged, and moving forward with large-scale models we don’t fully understand calls for caution. Challenges are posed from multiple directions, including but not limited to the trustworthiness of the generation, the interpretability and controllability of the models, the reliability of evaluation, and the integration of additional sources like knowledge and other modality. Considering these challenges will be crucial for realistic, ecologically valid deployment of summarization research.

Call for Papers

Both long paper (up to 8 pages with unlimited reference) and short paper (up to 4 pages with unlimited reference) are welcomed for submission!

A list of topics relevant to this workshop (but not limited to):

  • Abstractive summarization, extractive summarization and their integration
  • Summarization with pre-trained large models
  • Zero-shot/few-shot summarization
  • Fairness in summarization: faithfulness, bias, toxicity, and privacy-preserving
  • Interpretability and visualization of summarization systems
  • Controlled and tailored text generation
  • Knowledge/common sense injected summarization
  • Multiple text genres (News, tweets, product reviews, conversations, medical records, books, research articles, etc.)
  • Multimodal learning: information integration and aggregation across multiple modalities (text, speech, image, video)
  • Multilingual summarization
  • Semantic aspects of summarization (e.g., semantic representation, inference, validity)
  • Cognitive or psycholinguistic aspects of summarization (e.g., perceived readability, usability, etc.)
  • Development of novel algorithms (e.g., integrating neural and non-neural, distant supervision)
  • Development of new datasets and annotations
  • Development of new evaluation metrics

Submission Instructions

You are invited to submit your papers in our START/SoftConf submission portal. All the submitted papers have to be anonymous for double-blind review. The content of the paper should not be longer than 8 pages for long papers and 4 pages for short papers, strictly following the ACL 2023 style templates. Supplementary and appendices (either as separate files or appended after the main submission) are allowed. We encourage code link submissions for the camera-ready version.

NewSum 2023 will allow double submission as long as the authors make a decision before camera-ready. We will not consider any paper that overlaps significantly in content or results with papers that will be (or have been) published elsewhere. Authors submitting more than one paper to NewSum 2023 must ensure that their submissions do not overlap significantly (>25%) with each other in content or results. Authors can submit up to 100 MB of supplementary materials separately. Authors are highly encouraged to submit their codes for reproducibility purposes.

Important Dates:



Yue Dong

Yue Dong
University of California, Riverside, USA

Wen Xiao

Wen Xiao
University of British Columbia, Canada

Lu Wang

Wang Lu
University of Michigan, USA

Fei Liu

Fei Liu
Emory University, USA

Giuseppe Carenini

Giuseppe Carenini
University of British Columbia, Canada

Confirmed Spearkers

  • Jackie Cheung (McGill University)
  • Kathleen McKeown (Columbia University)
  • Xuanjing Huang (Fudan University)
  • Dragomir Radev (Yale University)
  • Wojciech Kryscinski (Salesforce)
  • Iz Beltagy (AI2)
  • Chenguang Zhu (Microsoft)



Program Committee

  • Manabu Okumura (Tokyo Institute of Technology)
  • Ido Dagan (Bar-Ilan University)
  • Ming Zhong (UIUC)
  • Kristjan Arumae (Qualtrics)
  • Pengcheng He (Microsoft Research)
  • Naoaki Okazaki (Tokyo Institute of Technology)
  • Zhe Hu (Baidu Inc)
  • Wojciech Kryscinski (Salesforce Research)
  • Haopeng Zhang (University of California Davis)
  • Hou Pong Chan (University of Macau)
  • Yang Liu (Microsoft)
  • Kaiqiang Song (Tencent AI Lab)
  • Juan-Manuel Torres-Moreno (LIA Avignon Université)
  • Jing Jiang (Singapore Management University)
  • Ziqiang Cao (Soochow University)
  • Margot Mieskes (University of Applied Sciences, Darmstadt)
  • Felice Dell'Orletta (Istituto di Linguistica Computazionale «A. Zampolli», CNR, Pisa, Italy)
  • Xinnuo Xu (University of Edinburgh)
  • Richard Evans (University of Wolverhampton)
  • Esau Villatoro-Tello (Idiap Research Institute)
  • Susana Bautista (Universidad Francisco de Vitoria)
  • Tobias Falke (Amazon Alexa)
  • Kellie Webster (Google)
  • Giulia Venturi (Institute for Computational Linguistics "A. Zampolli" (ILC-CNR)
  • Jessica Ouyang (University of Texas at Dallas)
  • Wencan Luo (Google)
  • Rui Zhang (Penn State University)
  • Linzi Xing (University of British Columbia)
  • Jiacheng Xu (Salesforce AI Research)
  • Tadashi Nomoto (National Institute of Japanese Literature)
  • Chao Zhao (UNC Chapel Hill)
  • Ori Shapira (Amazon)
  • Patrick Huber (UBC)
  • Florian Boudin (Nantes Université)
  • Xinyu Hua (Bloomberg)
  • Elena Lloret (University of Alicante, Spain)
  • Alexander Fabbri (Salesforce AI Research)
  • Tanya Goyal (UT Austin)
  • Yuntian Deng (Harvard University)
  • Maxime Peyrard (EPFL)