Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Stay on Topic, Please: Aligning User Comments to the Content of a News Article Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation

Authors : Adam Jatowt, I-Chen Hung, Michael Färber, Ricardo Campos, Masatoshi Yoshikawa

Published in: Advances in Information Retrieval

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Many archival collections have been recently digitized and made available to a wide public. The contained documents however tend to have limited attractiveness for ordinary users, since content may appear obsolete and uninteresting. Archival document collections can become more attractive for users if suitable content can be recommended to them. The purpose of this research is to propose a new research direction of Archival Content Suggestion to discover interesting content from long-term document archives that preserve information on society history and heritage. To realize this objective, we propose two unsupervised approaches for automatically discovering interesting sentences from news article archives. Our methods detect interesting content by comparing the information written in the past with one created in the present to make use of a surprise effect. Experiments on New York Times corpus show that our approaches effectively retrieve interesting content.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Mitigating the Position Bias of Transformer Models in Passage Re-ranking
next chapter Label Definitions Augmented Interaction Model for Legal Charge Prediction
Footnotes
5
One could imagine a service that automatically detects interesting sentences or headlines for broad topics and publishes them daily on web portals of underlying document archives.
 
10
We have also experimented with embedding models but they did not perform better.
 
12
We set n=5 as the number of top sentences returned for every top-ranked topic in Topic-based MRRW, and for each top-ranked topic pair in Topic Pair-based MRRW method and Topic co-occurrence methods.
 
13
Anecdotally, this particular example triggered recollections of childhood memories of one author. His grandparents owned a USSR-produced TV set and often warned him not to sit close to it when he visited their home. Only now, he could understand that the fears of his relatives were actually not without a substance. On a more general note, exploring news archives offers chances for learning about history, and might sometimes even lead to serendipitous discoveries and recollections as this example demonstrates.
 
Literature
1.
Adamopoulos, P., Tuzhilin, A.: On unexpectedness in recommender systems: or how to better expect the unexpected. ACM TIST 5(4), 54 (2015)
2.
Baldi, P., Itti, L.: Of bits and wows: a Bayesian theory of surprise with applications to attention. Neural Netw. 23(5), 649–666 (2010)CrossRef
3.
Berk, N.A., Gültekin, F.: The topics that students are curious about in the history lesson. Procedia-Soc. Behav. Sci. 15, 2785–2791 (2011)CrossRef
4.
Berlyne, D.E.: Conflict, arousal, and curiosity (1960)
5.
Boldi, P., Monti, C.: LlamaFur: learning latent category matrix to find unexpected relations in Wikipedia. In: Proceedings of WebScience, pp. 218–222. ACM (2016)
6.
Campos, R., Dias, G., Jorge, A.M., Jatowt, A.: Survey of temporal information retrieval and related applications. ACM Comput. Surv. 47(2), 15:1–15:41 (2014)
7.
Chen, Y.N., Metze, F.: Two-layer mutually reinforced random walk for improved multi-party meeting summarization. In: 2012 IEEE Spoken Language Technology Workshop (SLT), pp. 461–466. IEEE (2012)
8.
Costa, M., Silva, M.: Understanding the information needs of web archive users. In: The 10th International Web Archiving Workshop (2011)
9.
Derezinski, M., Rohanimanesh, K., Hydrie, A.: Discovering surprising documents with context-aware word representations. In: 23rd International Conference on Intelligent User Interfaces, pp. 31–35. ACM (2018)
10.
Färber, M.: Semantic Search for Novel Information, vol. 31. IOS Press, Amsterdam (2017)
11.
Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. (CSUR) 38(3), 9 (2006)CrossRef
12.
Gomes, D., Cruz, D., Miranda, J., Costa, M., Fontes, S.: Search the past with the Portuguese web archive. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 321–324 (2013)
13.
Hidi, S., Baird, W.: Interestingness-a neglected variable in discourse processing. Cogn. Sci. 10(2), 179–194 (1986)
14.
Itti, L., Baldi, P.F.: A principled approach to detecting surprising events in video. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Siego, CA, pp. 631–637, June 2005
15.
Itti, L., Baldi, P.: Bayesian surprise attracts human attention. Vision Res. 49(10), 1295–1306 (2009)CrossRef
16.
Kaminskas, M., Bridge, D.: Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Tran. Interact. Intell. Syst. (TiiS) 7(1), 1–42 (2016)
17.
Kanhabua, N., Anand, A.: Temporal information retrieval. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1235–1238 (2016)
18.
Koolen, M., Kamps, J.: Searching cultural heritage data: does structure help expert searchers? In: Adaptivity, Personalization and Fusion of Heterogeneous Information, pp. 152–155. Citeseer (2010)
19.
20.
Li, X., Croft, W.B.: Improving novelty detection for general topics using sentence level information patterns. In: Proceedings of CIKM, pp. 238–247. ACM (2006)
21.
Liu, B., Hsu, W., Mun, L.F., Lee, H.Y.: Finding interesting patterns using user expectations. IEEE Trans. Knowl. Data Eng. 11(6), 817–832 (1999)CrossRef
22.
Macrae, C.N., Bodenhausen, G.V.: Social cognition: thinking categorically about others. Annu. Rev. Psychol. 51(1), 93–120 (2000)CrossRef
23.
Padmanabhan, B., Tuzhilin, A.: Unexpectedness as a measure of interestingness in knowledge discovery. Decis. Support Syst. 27(3), 303–318 (1999)CrossRef
24.
25.
Pessent, E.: Is history irrelevant? Dissent Mag., pp. 1, June 1971
26.
Sandhaus, E.: The New York times annotated corpus. Linguist. Data Consortium Philadelphia 6(12), e26752 (2008)
27.
Schwartz, J.M., Cook, T.: Archives, records, and power: the making of modern memory. Arch. Sci. 2(1–2), 1–19 (2002)CrossRef
28.
Silberschatz, A., Tuzhilin, A.: What makes patterns interesting in knowledge discovery systems. IEEE TKDE 8(6), 970–974 (1996)
29.
Silveira, T., Zhang, M., Lin, X., Liu, Y., Ma, S.: How good your recommender system is? A survey on evaluations in recommendation. Int. J. Mach. Learn. Cybern. 10(5), 813–831 (2019)CrossRef
30.
Silvia, P.J.: What is interesting? Exploring the appraisal structure of interest. Emotion 5(1), 89 (2005)CrossRef
31.
Spyropoulou, E., De Bie, T., Boley, M.: Interesting pattern mining in multi-relational data. Data Min. Knowl. Discov. 28(3), 808–849 (2014)MathSciNetCrossRef
32.
Stiller, J.: A framework for classifying interactions in cultural heritage information systems. Int. J. Heritage Digital Era 1(1), 141–146 (2012)CrossRef
33.
Strauss, V.: Why so many students hate history - and what to do about it? The Washington Post (2017)
34.
35.
Trant, J.: Understanding searches of a contemporary art museum catalogue: a preliminary study. Report, Archives & Museum Informatics (2006)
36.
Tsukuda, K., Ohshima, H., Yamamoto, M., Iwasaki, H., Tanaka, K.: Discovering unexpected information on the basis of popularity/unpopularity analysis of coordinate objects and their relationships. In: Proceedings of SAC, pp. 878–885. ACM (2013)
37.
Tsurel, D., Pelleg, D., Guy, I., Shahaf, D.: Fun facts: automatic trivia fact extraction from Wikipedia. In: Proceedings of WSDM, pp. 345–354. ACM (2017)
38.
39.
Warwick, C., Terras, M., Huntington, P., Pappa, N.: If you build it will they come? The LAIRAH study: quantifying the use of online resources in the arts and humanities through statistical analysis of user log data. Literary Linguist. Comput. 23(1), 85–102 (2007)CrossRef
40.
Yannakakis, G.N., Liapis, A.: Searching for surprise. In: Proceedings of the International Conference on Computational Creativity (2016)
Metadata
Title
Exploding TV Sets and Disappointing Laptops: Suggesting Interesting Content in News Archives Based on Surprise Estimation
Authors
Adam Jatowt
I-Chen Hung
Michael Färber
Ricardo Campos
Masatoshi Yoshikawa
Copyright Year
2021
Book
Advances in Information Retrieval

Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-72113-8_17