Abstract
Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
An, N., Gui, F., Jin, L.Q., Ming, H., Yang, J.Y.: Towards better understanding older adults: a biography brief timeline extraction approach. Int. J. Hum. Comput. Interact. (in Press)
Ansah, J., Liu, L., Kang, W., Kwashie, S., Li, J., Li, J.: A graph is worth a thousand words: telling event stories using timeline summarization graphs. In: The World Wide Web Conference, pp. 2565–2571 (2019)
Chang, Y., Tang, J., Yin, D., Yamada, M., Liu, Y.: Timeline summarization from social media with life cycle models. In: IJCAI, pp. 3698–3704 (2016)
Che, W., Feng, Y., Qin, L., Liu, T.: N-LTP: a open-source neural chinese language technology platform with pretrained models. arXiv preprint arXiv:2009.11616 (2020)
Chen, L.C.: An effective lda-based time topic model to improve blog search performance. Inf. Process. Manag. 53(6), 1299–1319 (2017)
Ding, X., Li, Z., Liu, T., Liao, K.: Elg: an event logic graph. arXiv preprint arXiv:1907.08015 (2019)
Gottschalk, S., Demidova, E.: EventKG: a multilingual event-centric temporal knowledge graph. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 272–287. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93417-4_18
Gottschalk, S., Demidova, E.: EventKG+TL: creating cross-lingual timelines from an event-centric knowledge graph. In: Gangemi, A. et al. (eds.) ESWC 2018. LNCS, vol. 11155, pp. 164–169. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98192-5_31
Kantor, B., Globerson, A.: Coreference resolution with entity equalization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 673–677 (2019)
Li, J., Cardie, C.: Timeline generation: tracking individuals on Twitter. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 643–652 (2014)
Lin, C., Lin, C., Li, J., Wang, D., Chen, Y., Li, T.: Generating event storylines from microblogs. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 175–184 (2012)
Little, B.: Forgotten lives: exploring the history of learning disability (1998)
Liu, B., Han, F.X., Niu, D., Kong, L., Lai, K., Xu, Y.: Story forest: extracting events and telling stories from breaking news. ACM Trans. Knowl. Discov. Data (TKDD) 14(3), 1–28 (2020)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74–81 (2004)
Liu, B., Niu, D., Lai, K., Kong, L., Xu, Y.: Growing story forest online from massive breaking news. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 777–785 (2017)
Liu, X., Huang, H., Zhang, Y.: Open domain event extraction using neural latent variable models. arXiv preprint arXiv:1906.06947 (2019)
Lun, M.W.A.: The effectiveness of a life story program on stress reduction among chinese american family caregivers of older adults. Educ. Gerontol. 45(5), 334–340 (2019)
McKeown, J., Clarke, A., Ingleton, C., Ryan, T., Repper, J.: The use of life story work with people with dementia to enhance person-centred care. Int. J. Older People Nurs. 5(2), 148–158 (2010)
McKeown, J., Clarke, A., Repper, J.: Life story work in health and social care: systematic literature review. J. Adv. Nurs. 55(2), 237–247 (2006)
Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 198–207 (2005)
Minard, A.L.M., et al.: Semeval-2015 task 4: timeline: cross-document event ordering. In: 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 778–786 (2015)
Nallapati, R., Feng, A., Peng, F., Allan, J.: Event threading within news topics. In: Proceedings of the Thirteenth ACM International Conference on Information and Knowledge Management, pp. 446–453 (2004)
Phoenix, C., Sparkes, A.C.: Being fred: Big stories, small stories and the accomplishment of a positive ageing identity. Qual. Res. 9(2), 219–236 (2009)
Webster, J.D., Bohlmeijer, E.T., Westerhof, G.J.: Mapping the future of reminiscence: a conceptual guide for research and practice. Res. Aging 32(4), 527–564 (2010)
Xu, S., Wang, S., Zhang, Y.: Summarizing complex events: a cross-modal solution of storylines extraction and reconstruction. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1281–1291 (2013)
Yang, C.C., Shi, X., Wei, C.P.: Discovering event evolution graphs from news corpora. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 39(4), 850–863 (2009)
Acknowledgment
This work was partially supported by the National Natural Science Foundation of China (No. 62072153), the Anhui Provincial Key Technologies R&D Program (2022h11020015), the Program of Introducing Talents of Discipline to Universities (111 Program) (B14025).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Gui, F., Wu, X., Hu, M., Yang, J. (2022). Automatic Life Event Tree Generation for Older Adults. In: Duffy, V.G., Gao, Q., Zhou, J., Antona, M., Stephanidis, C. (eds) HCI International 2022 – Late Breaking Papers: HCI for Health, Well-being, Universal Access and Healthy Aging. HCII 2022. Lecture Notes in Computer Science, vol 13521. Springer, Cham. https://doi.org/10.1007/978-3-031-17902-0_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-17902-0_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17901-3
Online ISBN: 978-3-031-17902-0
eBook Packages: Computer ScienceComputer Science (R0)