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Paper Abstract and Keywords
Presentation 2026-03-20 10:30
Research on an automatic generation system for stakeholder-specific Safety Document using RAG
Hayato Kato, Minami Yoda, Yutaka Matsuno (Nihon Univ.) KBSE2025-63
Abstract (in Japanese) (See Japanese page) 
(in English) In the development of advanced systems such as automated driving, building safety consensus among stakeholders with diverse backgrounds—including executives, engineers, and safety auditors—has become a critical challenge. Conventional automated reporting tools often produce uniform summaries, failing to optimize information granularity or rhetorical style for specific audiences. This study proposes a stakeholder-adaptive Safety Status Report (SSR) generation system by integrating the sociolinguistic concept of "Audience Design" into the RAG-based retrieval and generation pipeline. The system takes Goal Structuring Notation (GSN) and design specifications as input and dynamically optimizes report generation through role-based dynamic $K$-value control, query expansion, and rhetorical strategy selection.We conducted a two-phase evaluation: retrieval accuracy (Phase 1) and generation quality via LLM-as-a-judge (Phase 2). Phase 1 demonstrated the system's robust adaptation, achieving high noise reduction for business roles (avg. P@K 84.5%, MRR 1.000) and comprehensive evidence extraction for technical roles (avg. Recall 88.9%, up to 100%). Phase 2 confirmed high-quality generation, with high scores in fluency (4.75/5) and GSN alignment (4.33/5), effectively balancing technical simplification for management (4.25/5) with high informativeness for experts (4.17/5). While positioned as a draft generation tool, the results verify its effectiveness in resolving information asymmetry in safety assurance and establishing a foundation for rapid, objective decision-making.
Keyword (in Japanese) (See Japanese page) 
(in English) Generative AI / RAG / Safety Status Report / Stakeholder / GSN / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 423, KBSE2025-63, pp. 85-90, March 2026.
Paper # KBSE2025-63 
Date of Issue 2026-03-12 (KBSE) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
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)
Download PDF KBSE2025-63

Conference Information
Committee KBSE  
Conference Date 2026-03-19 - 2026-03-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Prefectural General Welfare Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To KBSE 
Conference Code 2026-03-KBSE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Research on an automatic generation system for stakeholder-specific Safety Document using RAG 
Sub Title (in English)  
Keyword(1) Generative AI  
Keyword(2) RAG  
Keyword(3) Safety Status Report  
Keyword(4) Stakeholder  
Keyword(5) GSN  
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1st Author's Name Hayato Kato  
1st Author's Affiliation Nihon University (Nihon Univ.)
2nd Author's Name Minami Yoda  
2nd Author's Affiliation Nihon University (Nihon Univ.)
3rd Author's Name Yutaka Matsuno  
3rd Author's Affiliation Nihon University (Nihon Univ.)
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Speaker Author-1 
Date Time 2026-03-20 10:30:00 
Presentation Time 30 minutes 
Registration for KBSE 
Paper # KBSE2025-63 
Volume (vol) vol.125 
Number (no) no.423 
Page pp.85-90 
#Pages
Date of Issue 2026-03-12 (KBSE) 


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