[Curriculum Vitae]

Short Biography

Dr. Fei Liu is an associate professor of Computer Science at the University of Central Florida, where she leads the Natural Language Processing Group. Her research areas are natural language processing and deep learning, with special emphasis on automatic summarization. Her research aims to generate automated summaries from a massive amount of textual data to combat information overload. Building on recent advances in deep learning, Dr. Liu's group explores both extractive and abstractive approaches to generate informative, succinct and accurate summaries.

Dr. Liu was a postdoctoral fellow at Carnegie Mellon University, member of Noah's ARK. She worked as a senior scientist at Bosch Research, Palo Alto, California, which is one of the largest German companies building intelligent car systems and home appliances. Liu received her Ph.D. in Computer Science from the University of Texas at Dallas in 2011, supported by Erik Jonsson Distinguished Research Fellowship. She obtained her Bachelor and Masters degrees in Computer Science from Fudan University. Dr. Liu has published 60+ peer-reviewed papers in leading conferences and journals. She regularly serves on program committees of major international conferences. Liu was selected for the 2015 "MIT Rising Stars in EECS" program. Her work was nominated as Best Paper Award Finalist at WWW 2016 and Area Chair Favorite Paper at COLING 2018.

Research Interests

• Automatic Summarization
• Text Generation
• Deep Learning
• Machine Learning for NLP

Recent Publications [Google Scholar]

• StreamHover: Livestream Transcript Summarization and Annotation
  Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh and Fei Liu
  In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
  
• A Sliding-Window Approach to Automatic Creation of Meeting Minutes
  Jia Jin Koay, Alexander Roustai, Xiaojin Dai and Fei Liu
  In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), Student Research Workshop, 2021
  
• A New Approach to Overgenerating and Scoring Abstractive Summaries
  Kaiqiang Song, Bingqing Wang, Zhe Feng, Fei Liu
  In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2021
   Our summarizer produces outputs with various levels of detail: 
  
• CATE: Computation-aware Neural Architecture Encoding with Transformers
  Shen Yan, Kaiqiang Song, Fei Liu, Mi Zhang
  In Proceedings of the 38th International Conference on Machine Learning (ICML), 2021
  
• Learning to Fuse Sentences with Transformers for Summarization
  Logan Lebanoff, Franck Dernoncourt, Doo Soon Kim, Lidan Wang, Walter Chang and Fei Liu
  In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
  
• Better Highlighting: Creating Sub-Sentence Summary Highlights
  Sangwoo Cho, Kaiqiang Song, Chen Li, Dong Yu, Hassan Foroosh and Fei Liu
  In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
  
• Controlling the Amount of Verbatim Copying in Abstractive Summarization
  Kaiqiang Song, Bingqing Wang, Zhe Feng, Liu Ren and Fei Liu
  In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York City, New York, 2020
  
• Joint Parsing and Generation for Abstractive Summarization
  Kaiqiang Song, Logan Lebanoff, Qipeng Guo, Xipeng Qiu, Xiangyang Xue, Chen Li, Dong Yu and Fei Liu
  In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York City, New York, 2020
  
• Scoring Sentence Singletons and Pairs for Abstractive Summarization
  Logan Lebanoff, Kaiqiang Song, Franck Dernoncourt, Doo Soon Kim, Seokhwan Kim, Walter Chang and Fei Liu
  In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, 2019
  
• Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization
  Sangwoo Cho, Logan Lebanoff, Hassan Foroosh and Fei Liu
  In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy, 2019
  
• Guiding Extractive Summarization with Question-Answering Rewards
  Kristjan Arumae and Fei Liu
  In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), Minneapolis, Minnesota, 2019
  
• MoverScore: Text Generation Evaluating with Contextualized Embeddings and Earth Mover Distance
  Wei Zhao, Maxime Peyrard, Fei Liu, Yang Gao, Christian M. Meyer and Steffen Eger
  In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), Hong Kong, China, 2019
  
• Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
  Logan Lebanoff, Kaiqiang Song and Fei Liu
  In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium, 2018
  
• Structure-Infused Copy Mechanisms for Abstractive Summarization
  Kaiqiang Song, Lin Zhao and Fei Liu
  In Proceedings of the 27th International Conference on Computational Linguistics (COLING), Santa Fe, New Mexico, 2018
   Try our structure-aware sentence summarizer! 
  
• Abstract Meaning Representation for Multi-Document Summarization
  Kexin Liao, Logan Lebanoff and Fei Liu
  In Proceedings of the 27th International Conference on Computational Linguistics (COLING), Santa Fe, New Mexico, 2018
  [Area Chair Favorite Paper]
  
• Automatic Detection of Vague Words and Sentences in Privacy Policies
  Logan Lebanoff and Fei Liu
  In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium, 2018
  
• Reinforced Extractive Summarization with Question-Focused Rewards
  Kristjan Arumae and Fei Liu
  In Proceedings of ACL 2018 Student Research Workshop, Melbourne, Australia, 2018
  
• An Improved Phrase-based Approach to Annotating and Summarizing Student Course Responses
  Wencan Luo, Fei Liu, Diane Litman
  In Proceedings of the 26th International Conference on Computational Linguistics (COLING), Osaka, Japan, 2016
  
• Automatic Summarization of Student Course Feedback
  Wencan Luo, Fei Liu, Zitao Liu, Diane Litman
  In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), San Diego, California, 2016
  
• Toward Abstractive Summarization Using Semantic Representations
  Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh and Noah A. Smith
  In Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL), Denver, Colorado, 2015
  
• Extractive Summarization by Maximizing Semantic Volume
  Dani Yogatama, Fei Liu, Noah A. Smith
  In Proceedings of the Conference on Empirical Methods on Natural Language Processing (EMNLP), Lisboa, Portugal, 2015
  

Professional Service

• Senior area chair, EMNLP 2021
• Area chair, NAACL 2021, ACL 2021
• General Publications Chair, EMNLP 2020
• Senior area chair, ACL 2020, Summarization
• Senior program committee, AAAI 2020
• Area chair, ACL 2019, Summarization Track
• Area chair, NAACL 2019, Summarization Track
• Publications chair, EMNLP 2019
• Demonstrations chair, ACL 2018
• Area chair, EMNLP 2018
• Area chair, IJCNLP 2017
• Co-organizer, EMNLP Workshop on New Frontiers in Summarization, 2017, 2019, 2021
• NSF/IIS Panel and Ad-hoc Reviewer, 2016, 2019, 2021

Invited Talks

• Rice University, December 2021
• Emory University, September 2021
• Qualtrics Engineering, September 2021
• Google Research, London, April 2021
• Microsoft Cognitive Services Research Group, October 2020
• Georgetown University, October 2020

Sponsors

• Amazon AWS Machine Learning Research Award
• RI: Small: Towards Abstractive Summarization That Preserves the Original Meaning