📰 GEM 2022 — accepted papers

GEM 2022 is the 2022 edition of the Workshop on Generation, Evaluation and Metrics (GEM), held in Abu Dhabi, United Arab Emirates (Hybrid), December 2022 (publisher: Association for Computational Linguistics). The ACL Anthology lists 45 papers of it; our library holds 1 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam SearchDaniel King, Zejiang Shen, Nishant Subramani et al.26 citations
2. Control Prefixes for Parameter-Efficient Text GenerationJordan Clive, Kris Cao, Marek Rei20 citations
3. What Makes Data-to-Text Generation Hard for Pretrained Language Models?Moniba Keymanesh, Adrian Benton, Mark Dredze12 citations
4. Factual Error Correction for Abstractive Summaries Using Entity RetrievalHwanhee Lee, Cheoneum Park, Seunghyun Yoon et al.10 citations
5. Improving abstractive summarization with energy-based re-rankingDiogo Pernes, Afonso Mendes, André F. T. Martins9 citations
6. Semantic Similarity as a Window into Vector- and Graph-Based MetricsWai Ching Leung, Shira Wein, Nathan Schneider8 citations
7. Analyzing Multi-Task Learning for Abstractive Text SummarizationFrederic Kirstein, Jan Philip Wahle, Terry Ruas et al.7 citations
8. Weakly Supervised Context-based Interview Question GenerationSamiran Pal, Kaamraan Khan, Avinash Kumar Singh et al.7 citations
9. Enhancing and Evaluating the Grammatical Framework Approach to Logic-to-Text GenerationEduardo Calò, Elze van der Werf, Albert Gatt et al.6 citations
10. Human perceiving behavior modeling in evaluation of code generation modelsSergey Kovalchuk, Vadim Lomshakov, Artem Aliev6 citations

All accepted papers (45)

The most cited first; 1 with our summary link to their page here, the others to the original.

1. Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam SearchDaniel King, Zejiang Shen, Nishant Subramani et al.26 citations
2. Control Prefixes for Parameter-Efficient Text GenerationJordan Clive, Kris Cao, Marek Rei20 citations
3. What Makes Data-to-Text Generation Hard for Pretrained Language Models?Moniba Keymanesh, Adrian Benton, Mark Dredze12 citations
4. Factual Error Correction for Abstractive Summaries Using Entity RetrievalHwanhee Lee, Cheoneum Park, Seunghyun Yoon et al.10 citations
5. Improving abstractive summarization with energy-based re-rankingDiogo Pernes, Afonso Mendes, André F. T. Martins9 citations
6. Semantic Similarity as a Window into Vector- and Graph-Based MetricsWai Ching Leung, Shira Wein, Nathan Schneider8 citations
7. Analyzing Multi-Task Learning for Abstractive Text SummarizationFrederic Kirstein, Jan Philip Wahle, Terry Ruas et al.7 citations
8. Weakly Supervised Context-based Interview Question GenerationSamiran Pal, Kaamraan Khan, Avinash Kumar Singh et al.7 citations
9. Enhancing and Evaluating the Grammatical Framework Approach to Logic-to-Text GenerationEduardo Calò, Elze van der Werf, Albert Gatt et al.6 citations
10. Human perceiving behavior modeling in evaluation of code generation modelsSergey Kovalchuk, Vadim Lomshakov, Artem Aliev6 citations
11. Measuring the Measuring Tools: An Automatic Evaluation of Semantic Metrics for Text CorporaGeorge Kour, Samuel Ackerman, Eitan Daniel Farchi et al.6 citations
12. T5QL: Taming language models for SQL generationSamuel David Arcadinho, David Aparicio, Hugo Veiga et al.6 citations
13. A Survey of Recent Error Annotation Schemes for Automatically Generated TextRudali Huidrom, Anya Belz5 citations
14. Answerability: A custom metric for evaluating chatbot performancePranav Gupta, Anand A. Rajasekar, Amisha Patel et al.5 citations
15. Revisiting text decomposition methods for NLI-based factuality scoring of summariesJohn Glover, Federico Fancellu, Vasudevan Jagannathan et al.5 citations
16. Nearest Neighbor Language Models for Stylistic Controllable GenerationSeverino Trotta, Lucie Flek, Charles Welch4 citations
17. Improved Evaluation of Automatic Source Code SummarisationJesse Phillips, David Bowes, Mahmoud El-Haj et al.3 citations
18. Most NLG is Low-Resource: here's what we can do about itDavid M. Howcroft, Dimitra Gkatzia3 citations
19. Narrative Why-Question Answering: A Review of Challenges and DatasetsEmil Kalbaliyev, Kairit Sirts3 citations
20. On reporting scores and agreement for error annotation tasksMaja Popović, Anya Belz3 citations
21. Unsupervised Token-level Hallucination Detection from Summary Generation By-productsAndreas Marfurt, James Henderson3 citations
22. 20Q: Overlap-Free World Knowledge Benchmark for Language ModelsMaxime De Bruyn, Ehsan Lotfi, Jeska Buhmann et al.2 citations
23. Error Analysis of ToTTo Table-to-Text Neural NLG ModelsBarkavi Sundararajan, Somayajulu Sripada, Ehud Reiter2 citations
24. Evaluation of Response Generation Models: Shouldn't It Be Shareable and Replicable?Seyed Mahed Mousavi, Gabriel Roccabruna, Michela Lorandi et al.2 citations
25. Generating Coherent Narratives with Subtopic Planning to Answer How-to QuestionsPengshan Cai, Mo Yu, Fei Liu et al.2 citations

See all 45 papers in GEM 2022 — accepted papers →

📄 Papers with our summary in our library (1)

The most cited first (OpenAlex citations plus our library's).

1. Improving Dialogue Act Recognition with Augmented DataKhyati Mahajan, Soham Parikh, Quaizar Vohra, et al.2022 · 2 citations

🧭 Top topics

Topics of its works in our library, with the number of works on each.

💬Speech act theory (1)🗨️Dialogue systems (1)

📚 Fields

🎭Arts and Humanities (1)🖥️Computer Science (1)

🧑‍🔬 Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

🏛️ Top institutions

Institutions on the bylines of its works here, by the number of works.

🏢ServiceNowSanta Clara · United States1 work here

Place and dates from the event's site or the ACL Anthology; works and citations from OpenAlex and our library.

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