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Toru Fujino

dblp:203/8718 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Language models and text generation › text summarization
extractive summarization
0.312017
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
0.312017
Extractive Summarization Using Multi-Task Learning with Document Classification · EMNLP 2017
Natural language and speech › Information extraction and text analysis
text classification
0.112017
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
YearPublicationVenuePosition
2017 Extractive Summarization Using Multi-Task Learning with Document Classification
abstract
The 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
EMNLP2