Demo Data from VLDB 2026 / reviewers in the wild / expert
Toru Fujino
dblp:203/8718
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—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.
| Artificial intelligence
1 paper |
Language models and text generation · 87% Information extraction and text analysis · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Language models and text generation › text summarization
extractive summarization |
0.3 | 1 | 2017 | Extractive Summarization Using Multi-Task Learning with Document Classification · EMNLP 2017 |
Natural language and speech › Language models and text generation › text summarization
multi-task summarization |
0.3 | 1 | 2017 | Extractive Summarization Using Multi-Task Learning with Document Classification · EMNLP 2017 |
Natural language and speech › Information extraction and text analysis
text classification |
0.1 | 1 | 2017 | Extractive Summarization Using Multi-Task Learning with Document Classification · EMNLP 2017 |
Methods — techniques the papers use, named apart from their topics
multi-task learning · 0.3curriculum learning · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Extractive Summarization Using Multi-Task Learning with Document ClassificationabstractThe need for automatic document summarization that can be used for practical applications is increasing rapidly.In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information.Our work is aimed at single document summarization using small amounts of reference summaries.In particular, we address document summarization in the framework of multitask learning using curriculum learning for sentence extraction and document classification.The proposed framework enables us to obtain better feature representations to extract sentences from documents.We evaluate our proposed summarization method on two datasets: financial report and news corpus.Experimental results demonstrate that our summarizers achieve performance that is comparable to stateof-the-art systems. Masaru Isonuma, Toru Fujino, Junichiro Mori, Yutaka Matsuo, Ichiro Sakata |
EMNLP | 2 |