Sequence Summarization is a sequence mining technique that identifies and constructs a concise, representative subset or abstraction of sequential patterns while preserving essential structural and temporal characteristics of the original sequence data.
51
Publications
37K
Citations
144
Authors
61
Institutions
Publications per year
2017–2026
17
Publications
51
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Myle Ott, Sergey Edunov, Alexei Baevski et al. · 2019 · 2.5K citations · Full text
Ramesh Nallapati, Feifei Zhai, Bowen Zhou · Proceedings of the AAAI Conference on Artificial Intelligence · 2017 · 958 citations · Full text
1–5 of 51
Authors
144
- JK
3
h-index
- ET
3
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- MAMiltiadis Allamanis
Microsoft Research (United Kingdom), United Kingdom
2 publications · 194 citations
2
h-index
- FZ
2
h-index
- MO
2
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1–5 of 144
Institutions
61
IBM (United States)
Armonk, United States
3 h-index
8 publications
5.2K citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
3 publications
328 citations
Universidad Autónoma de Madrid
Madrid, Spain
2 h-index
4 publications
118 citations
Microsoft (United States)
Redmond, United States
2 h-index
3 publications
193 citations
Google (United States)
Mountain View, United States
2 h-index
4 publications
601 citations
University College London
London, United Kingdom
2 h-index
7 publications
3.2K citations
University of Pennsylvania
Philadelphia, United States
2 h-index
4 publications
1.2K citations
IBM Research - Almaden
San Jose, United States
2 h-index
3 publications
58 citations
1–8 of 61
Venues
- arXiv (Cornell University)
Repository · 9 publications · 1.4K citations
9
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- Bioinformatics
2 publications · 29.2K citations
2
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- IEEE Transactions on Circuits and Systems for Video Technology
1 publications · 35 citations
1
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- ACM SIGMOD Record
1 publications · 162 citations
1
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- ACM Transactions on Knowledge Discovery from Data
TKDD · 1 publications · 58 citations
1
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