Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
20181.0k citationsPiyush Sharma, Nan Ding et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Radu Soricut'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 Radu Soricut with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Radu Soricut more than expected).
This network shows the impact of papers produced by Radu Soricut. 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 Radu Soricut. The network helps show where Radu Soricut may publish in the future.
Co-authorship network of co-authors of Radu Soricut
This figure shows the co-authorship network connecting the top 25 collaborators of Radu Soricut.
A scholar is included among the top collaborators of Radu Soricut based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Radu Soricut. Radu Soricut is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Changpinyo, Soravit, et al.. (2022). All You May Need for VQA are Image Captions. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1947–1963.25 indexed citations
Bojar, Ondřej, Christian Buck, Chris Callison-Burch, et al.. (2013). Findings of the 2013 Workshop on Statistical Machine Translation. UvA-DARE (University of Amsterdam). 1–44.179 indexed citations
15.
Bojar, Ondřej, Christian Buck, Chris Callison-Burch, et al.. (2013). Proceedings of the Eighth Workshop on Statistical Machine Translation. Workshop on Statistical Machine Translation.6 indexed citations
16.
Callison-Burch, Chris, Philipp Koehn, Christof Monz, et al.. (2012). Proceedings of the Seventh Workshop on Statistical Machine Translation. Workshop on Statistical Machine Translation.30 indexed citations
Soricut, Radu. (2005). Natural language generation for text-to-text applications using an information-slim representation. National Conference on Artificial Intelligence. 1662–1663.2 indexed citations
19.
Soricut, Radu & Eric Brill. (2004). Automatic Question Answering: Beyond the Factoid.. North American Chapter of the Association for Computational Linguistics. 57–64.68 indexed citations
20.
Echihabi, Abdessamad, et al.. (2002). GLEANS: A Generator of Logical Extracts and Abstracts for Nice Summaries.5 indexed citations
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.