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Yongmin Shin

dblp:270/6726 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Information retrieval › text summarization
multi-document summarization
0.412020
WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020
Information retrieval
text summarization
0.412020
WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020
Information retrieval › text summarization › temporal summarization
timeline summarization
0.412020
WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020
Information retrieval › text summarization › multi-document summarization
topic-focused summarization
0.412020
WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020
Data mining › text mining
topic detection
0.112020
WIRE: An Automated Report Generation System using Topical and Temporal Summarization · SIGIR 2020
Data mining › text mining
topic modeling
0.112020
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
YearPublicationVenuePosition
2020 WIRE: An Automated Report Generation System using Topical and Temporal Summarization
abstract
The 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
SIGIR2