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Hans-Martin Ramsl

dblp:169/3300 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 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 · 75% Data mining · 25%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › text summarization
extractive summarization		 0.212015
Topical Coherence for Graph-based Extractive Summarization · EMNLP 2015
Information retrieval › text summarization
graph-based summarization		 0.212015
Topical Coherence for Graph-based Extractive Summarization · EMNLP 2015
Information retrieval
text summarization		 0.212015
Topical Coherence for Graph-based Extractive Summarization · EMNLP 2015
Data mining › text mining › topic model › topic model evaluation
topic coherence		 0.212015
Topical Coherence for Graph-based Extractive Summarization · EMNLP 2015

Methods — techniques the papers use, named apart from their topics

topic modeling · 0.2integer linear programming · 0.2
YearPublicationVenuePosition
2015 Topical Coherence for Graph-based Extractive Summarization
abstract
We present an approach for extractive single-document summarization. Our ap-proach is based on a weighted graphical representation of documents obtained by topic modeling. We optimize importance, coherence and non-redundancy simulta-neously using ILP. We compare ROUGE scores of our system with state-of-the-art results on scientific articles from PLOS Medicine and on DUC 2002 data. Hu-man judges evaluate the coherence of sum-maries generated by our system in com-parision to two baselines. Our approach obtains competitive performance. 1
Daraksha Parveen, Hans-Martin Ramsl, Michael Strube 0001
 EMNLP2