SuDocu: Summarizing Documents by Example
Summary: SuDocu learns personalized summarization intent from a few user-written examples, avoiding explicit preference specification or costly training data. It expresses the inferred intent as an explainable package query, combining topic modeling, semantic similarity, and in-database optimization. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Anna Fariha (University of Massachusetts Amherst)
- 2. Matteo Brucato (University of Massachusetts Amherst)
- 3. Peter J. Haas (University of Massachusetts Amherst)
- 4. Alexandra Meliou (University of Massachusetts Amherst)
BibTeX Citation
@article{fariha_vldb20,
title = {{SuDocu: Summarizing Documents by Example}},
author = {Fariha, Anna and Brucato, Matteo and Haas, Peter J. and Meliou, Alexandra},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2861--2864},
doi = {10.14778/3415478.3415494},
url = {https://doi.org/10.14778/3415478.3415494},
year = {2020}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,192 | Data-Driven Insight Synthesis for Multi-Dimensional Data | 2024 | VLDB | 5.2110714e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,504 | Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity | 2019 | VLDB | 7.2516839e-05 |
| 6,960 | Scalable Package Queries in Relational Database Systems | 2016 | VLDB | 5.6333868e-05 |
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