Toward Better Understanding Older Adults: A Biography Brief Timeline Extraction Approach

Abstract

Studies have shown that life stories can help caregivers better understand older adults, leading to better care. The original life stories are often redundant and disordered, hindering the discovery of valuable information. There has been little research on organizing older adults’ life stories automatically. This article proposes the ALBERT Based Text Extraction Network (ABTE-NET) to generate valuable event timelines to address this problem. To evaluate the proposed method, we created an Older Adults’ Life Story dataset with 80 older adults’ life stories. In experiments, we verify that the timelines generated by ABTE-NET have good readability and summarization for life stories. A survey of 33 caregivers from two nursing homes shows that timelines can help caregivers understand older adults and build positive relationships, just like life stories. More importantly, timelines are better organized and more concise than original life stories, reducing the cognitive load and helping caregivers form a preliminary understanding of the older adult quickly.

Acknowledgements

We thank Professor Tianshu Pan at Fudan University and Professor Honglin Chen at the University of Eastern Finland for their insightful discussions and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The OALS dataset is available at https://github.com/gerontech-hfut/Timeline-Generation.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China (No. 62072153), the Anhui Provincial Key Technologies R&D Program (1804b06020378), and the Program of Introducing Talents of Discipline to Universities (111 Program) (B14025).

Notes on contributors

Ning An

Ning An has a PhD in Computer Science and Engineering from Pennsylvania State University, the USA, and is a Fellow of the International Academy of Health Information Sciences (FIAHSI). He is the founding director of the Gerontechnology Lab at the Hefei University of Technology.

Fang Gui

Fang Gui is a PhD student from the Hefei University of Technology, Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education, School of Computer Science and Information Engineering. Her research interests are well-being technologies for older adults and natural language processing.

Liuqi Jin

Liuqi Jin is a PhD student from the Hefei University of Technology, Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education, School of Computer Science and Information Engineering. His research is the knowledge graph for eldercare, and he has rich experience in AI project implementation.

Hong Ming

Hong Ming is a PhD student from the Hefei University of Technology, Key Laboratory of Knowledge Engineering with Big Data of the Ministry of Education, School of Computer Science and Information Engineering. His research interests include pretrained language models, named entity classification, and few-shot learning.

Jiaoyun Yang

Jiaoyun Yang holds a PhD from the University of Science and Technology of China. He is currently an associate professor at the Hefei University of Technology. His research interests include algorithm design and analysis, machine learning, and medical information mining.