Daniel Gillick
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
- Advanced Graph Neural Networks
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- Data Quality and Management
Papers in
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- Natural Language Processing Techniques 9
- Topic Modeling 8
- Advanced Text Analysis Techniques 2
- Text Readability and Simplification 1
- Authorship Attribution and Profiling 1
- Algorithms and Data Compression 1
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- Multimodal Machine Learning Applications 1
- Co-authors
- Nevena Lazic (1 shared paper)Dani Yogatama (1 shared paper)Dilek Hakkani‐Tür (3 shared papers)Benoît Favre (1 shared paper)Nelson Morgan (1 shared paper)David J. Weiss (1 shared paper)Yuan Zhang (1 shared paper)Jason Riesa (1 shared paper)
- Partner nations
- United States
In The Last Decade
Daniel Gillick
9 papers receiving 221 citations
Peers
Comparison fields: 5 of 29
- Artificial Intelligence 228
- Management Science and Operations Research 22
- Computer Vision and Pattern Recognition 27
- Information Systems 27
- Computer Science Applications 4
Countries citing papers authored by Daniel Gillick
This map shows the geographic impact of Daniel Gillick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Gillick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Gillick more than expected).
Fields of papers citing papers by Daniel Gillick
This network shows the impact of papers produced by Daniel Gillick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Gillick. The network helps show where Daniel Gillick may publish in the future.
Co-authors
The 16 scholars most cited alongside Daniel Gillick, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2015 | 84 | |
| 2 | The ICSI Summarization System at TAC 2008. | 2008 | 63 |
| 3 | 2014 | 36 | |
| 4 | 2018 | 23 | |
| 5 | 2023 | 12 | |
| 6 | 2016 | 10 | |
| 7 | The elements of automatic summarization | 2011 | 8 |
| 8 | 2007 | 6 | |
| 9 | 2009 | 1 |
About Daniel Gillick
Daniel Gillick is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Management Science and Operations Research and Infectious Diseases, having authored 9 papers that have together received 243 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (9 papers), Topic Modeling (8 papers), Advanced Text Analysis Techniques (2 papers), Multimodal Machine Learning Applications (1 paper), Text Readability and Simplification (1 paper), Language, Metaphor, and Cognition (1 paper), Authorship Attribution and Profiling (1 paper) and Algorithms and Data Compression (1 paper). The work is most often cited by research in Artificial Intelligence (228 citations), Management Science and Operations Research (22 citations), Computer Vision and Pattern Recognition (27 citations), Information Systems (27 citations) and Computer Science Applications (4 citations). Daniel Gillick has collaborated with scholars based in United States. Frequent co-authors include Nevena Lazic, Dani Yogatama, Dilek Hakkani‐Tür, Benoît Favre, Nelson Morgan, David J. Weiss, Yuan Zhang, Jason Riesa, Jason Baldridge and Zhengzhong Liu. Their work appears in journals such as Computer Speech & Language and Theory and applications of categories.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.