Demo Data from EDBT 2026 / reviewers in the wild / expert
Yongmin Shin
dblp:270/6726
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 87% Data mining · 13% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › text summarization
multi-document summarization |
0.4 | 1 | 2020 | WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020 |
Information retrieval
text summarization |
0.4 | 1 | 2020 | WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020 |
Information retrieval › text summarization › temporal summarization
timeline summarization |
0.4 | 1 | 2020 | WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020 |
Information retrieval › text summarization › multi-document summarization
topic-focused summarization |
0.4 | 1 | 2020 | WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020 |
Data mining › text mining
topic detection |
0.1 | 1 | 2020 | WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020 |
Data mining › text mining
topic modeling |
0.1 | 1 | 2020 | WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020 |
Methods — techniques the papers use, named apart from their topics
topic model · 0.4timeline summarizer · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | WIRE: An Automated Report Generation System using Topical and Temporal SummarizationabstractThe demand for a tool for summarizing emerging topics is increasing in modern life since the tool can deliver well-organized information to its users. Even though there are already a number of successful search systems, the system which automatically summarizes and organizes the content of emerging topics is still in its infancy. To fulfill such demand, we introduce an automated report generation system that generates a well-summarized human-readable report for emerging topics. In this report generation system, emerging topics are automatically discovered by a topic model and news articles are indexed by the discovered topics. Then, a topical summary and a timeline summary for each topic is generated by a topical multi-document summarizer and a timeline summarizer respectively. In order to enhance the apprehensibility of the users, the proposed report system provides two report modes. One is Today's Briefing which summarizes five discovered topics of every day, and the other is Full Report which shows a long-term view of each topic with a detailed topical summary and an important event timeline. Yunseok Noh, Yongmin Shin, Junmo Park, A.-Yeong Kim, Su-Jeong Choi, Hyun-Je Song, Seong-Bae Park, Se-Young Park |
SIGIR | 2 |