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Paper Abstract and Keywords
Presentation 2022-09-13 15:30
Extraction of sentences representing company-specific competitive advantages from integrated reports
Yuta Sugawara, Hiroyuki Sakai, Kengo Enami (Seikei), Kaito Takano, Kei Nakagawa (Nomura Asset Management) NLC2022-7
Abstract (in Japanese) (See Japanese page) 
(in English) In this research, we propose a method for extracting sentences representing company-specific competitive advantages from integrated reports by deep learning. For example, our method extracts sentences that include the company's strengths, specific management strategies, and business development such as “The automotive products business of the Sumitomo Science & Engineering Group handles anti-vibration rubber and automotive hoses, which boast the largest market share in the world, as well as interior and sound insulation products that contribute to ride comfort and safety.”. Form these sentences, it is possible to understand a company's unique strengths and the points in which the company is superior among companies in the same industry. Our method uses BERT as a classifier with fine tuning, so that even a small amount of training data can be classified with a reasonably high accuracy. In addition, our method restricts the classification results by the degree of similarity between sentences, and attained 0.713 precision.
Keyword (in Japanese) (See Japanese page) 
(in English) integrated reports / sentence identification / machine learning / clue phrases / information extraction / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 180, NLC2022-7, pp. 13-18, Sept. 2022.
Paper # NLC2022-7 
Date of Issue 2022-09-06 (NLC) 
ISSN Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee NLC  
Conference Date 2022-09-13 - 2022-09-13 
Place (in Japanese) (See Japanese page) 
Place (in English) Keio Univ. Yagami Campus. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) The 19th Text Analytics Symposium 
Paper Information
Registration To NLC 
Conference Code 2022-09-NLC 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Extraction of sentences representing company-specific competitive advantages from integrated reports 
Sub Title (in English)  
Keyword(1) integrated reports  
Keyword(2) sentence identification  
Keyword(3) machine learning  
Keyword(4) clue phrases  
Keyword(5) information extraction  
1st Author's Name Yuta Sugawara  
1st Author's Affiliation Seikei University (Seikei)
2nd Author's Name Hiroyuki Sakai  
2nd Author's Affiliation Seikei University (Seikei)
3rd Author's Name Kengo Enami  
3rd Author's Affiliation Seikei University (Seikei)
4th Author's Name Kaito Takano  
4th Author's Affiliation Nomura Asset Management Co. (Nomura Asset Management)
5th Author's Name Kei Nakagawa  
5th Author's Affiliation Nomura Asset Management Co. (Nomura Asset Management)
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Speaker Author-1 
Date Time 2022-09-13 15:30:00 
Presentation Time 25 minutes 
Registration for NLC 
Paper # NLC2022-7 
Volume (vol) vol.122 
Number (no) no.180 
Page pp.13-18 
Date of Issue 2022-09-06 (NLC) 

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