The Design and Implementation of APLOS: An Automated PoLicy DOcument Summarisation System

Many institutions write and publish policy documents to inform stakeholders or citizens about priority areas and legislations or regulations governing issues such as the environment, agriculture, food safety and business operations, to mention a few. These documents are often lengthy, have different formats, are full of jargon, may have many versions, and require domain knowledge to understand the policy issue. These characteristics make it challenging to develop a personalised tool for users to generate concise summaries based on their domain knowledge, refine the summaries without compromising the original meaning of the policy, and find related policies. To solve these problems, we leverage advances in foundation models (FMs) and Large Language Models (LLMs) to develop a tool called "APLOS" for summarising and gaining insights from policy documents. The architecture, implementation, and natural language processing challenges we faced in developing the tool and the future directions we are undertaking to address the challenges are presented and discussed.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/fllm63129.2024.10852442
Primary Topic
Algorithms and Data Compression
Type
article
Field-Weighted Citation Impact
0.00

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article

The Design and Implementation of APLOS: An Automated PoLicy DOcument Summarisation System

Yongli Mou, Alexander Neumann, Vassilios Peristeras, Sulayman K. Sowe et al.
Algorithms and Data Compression
article

The Design and Implementation of APLOS: An Automated PoLicy DOcument Summarisation System

Yongli Mou, Alexander Neumann, Vassilios Peristeras, Sulayman K. Sowe, Stefan Decker, Tobias Kiel
article en

Abstract

Many institutions write and publish policy documents to inform stakeholders or citizens about priority areas and legislations or regulations governing issues such as the environment, agriculture, food safety and business operations, to mention a few. These documents are often lengthy, have different formats, are full of jargon, may have many versions, and require domain knowledge to understand the policy issue. These characteristics make it challenging to develop a personalised tool for users to generate concise summaries based on their domain knowledge, refine the summaries without compromising the original meaning of the policy, and find related policies. To solve these problems, we leverage advances in foundation models (FMs) and Large Language Models (LLMs) to develop a tool called "APLOS" for summarising and gaining insights from policy documents. The architecture, implementation, and natural language processing challenges we faced in developing the tool and the future directions we are undertaking to address the challenges are presented and discussed.

International Hellenic University (GR), RWTH Aachen University (DE)
Ministry of Education
Openalex Percentile: Top 76%
Algorithms and Data Compression
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