Abstract
News articles generated by online media are a major source of information. In this work, we present News Monitor, a framework that automatically collects news articles from a variety of web pages and performs various analysis tasks. The framework initially identifies fresh news and clusters articles about the same incidents. For every story, it extracts a Knowledge Base (KB) using open information extraction techniques and utilizes this KB in order to build a summary for the user. News Monitor allows the users to query the article in natural language using the state-of-the-art framework BERT. Nevertheless, it allows the user to perform queries also in the KB in order to identify relevant articles. Finally, News Monitor crawls Twitter using a dynamic set of keywords in order to retrieve relevant messages. The framework is distributed, online and performs analysis in real-time.
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
Notes
- 1.
System demonstration available in: http://195.134.67.89/news_monitor/.
References
Pre-trained BERT model. https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad (2018). Accessed 20 Jan 2021
Abdelhaq, H., Sengstock, C., Gertz, M.: Eventweet: online localized event detection from twitter. Proc. VLDB Endow. 6(12), 1326–1329 (2013)
Christensen, J., Soderland, S., Etzioni, O.: An analysis of open information extraction based on semantic role labeling. In: Proceedings of the Sixth International Conference on Knowledge Capture, pp. 113–120 (2011)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://www.aclweb.org/anthology/N19-1423
Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam, M.: Open information extraction: the second generation. IJCAI 11, 3–10 (2011)
Leban, G., Fortuna, B., Brank, J., Grobelnik, M.: Event registry: learning about world events from news. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 107–110 (2014)
Mathioudakis, M., Koudas, N.: Twittermonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 1155–1158 (2010)
Meladianos, P., Nikolentzos, G., Rousseau, F., Stavrakas, Y., Vazirgiannis, M.: Degeneracy-based real-time sub-event detection in twitter stream. ICWSM 15, 248–257 (2015)
Moran, S., McCreadie, R., Macdonald, C., Ounis, I.: Enhancing first story detection using word embeddings. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 821–824 (2016)
Pal, H.: Demonyms and compound relational nouns in nominal open IE. In: Proceedings of the 5th Workshop on Automated Knowledge Base Construction, pp. 35–39 (2016)
Panagiotou, N.E., Akkaya, C., Tsioutsiouliklis, K., Kalogeraki, V., Gunopulos, D.: A general framework for first story detection utilizing entities and their relations. IEEE Trans. Knowl. Data Eng. 1, 1 (2020)
Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to twitter. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 181–189 (2010)
Petrović, S., Osborne, M., Lavrenko, V.: Using paraphrases for improving first story detection in news and twitter. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 338–346 (2012)
Saeed, Z., Ayaz Abbasi, R., Razzak, I.: EveSense: what can you sense from twitter? In: Jose, J.M., et al. (eds.) Advances in Information Retrieval, pp. 491–495. Springer, Cham (2020)
Sankaranarayanan, J., Samet, H., Teitler, B.E., Lieberman, M.D., Sperling, J.: Twitterstand: news in tweets. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 42–51 (2009)
Saravanou, A., Katakis, I., Valkanas, G., Gunopulos, D.: Detection and delineation of events and sub-events in social networks. In: ICDE, pp. 1348–1351 (2018)
Saravanou, A., Katakis, I., Valkanas, G., Kalogeraki, V., Gunopulos, D.: Revealing the hidden links in content networks: an application to event discovery. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2283–2286 (2017)
Saravanou, A., Stefanoni, G., Meij, E.: Identifying notable news stories. In: Jose, J.M., et al. (eds.) ECIR 2020. LNCS, vol. 12036, pp. 352–358. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45442-5_44
Saravanou, A., Valkanas, G., Gunopulos, D., Andrienko, G.: Twitter floods when it rains: a case study of the UK floods in early 2014. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1233–1238 (2015)
Watanabe, K., Ochi, M., Okabe, M., Onai, R.: Jasmine: a real-time local-event detection system based on geolocation information propagated to microblogs. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2541–2544 (2011)
Acknowledgments
The present work was co-funded by the European Union and Greek national funds through the Operational Program “Human Resources Development, Education and Lifelong Learning” (NSRF 2014-2020), under the call “Supporting Researchers with an Emphasis on Young Researchers - Cycle B” (MIS:5048149).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Saravanou, A., Panagiotou, N., Gunopulos, D. (2021). News Monitor: A Framework for Querying News in Real Time. In: Hiemstra, D., Moens, MF., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds) Advances in Information Retrieval. ECIR 2021. Lecture Notes in Computer Science(), vol 12657. Springer, Cham. https://doi.org/10.1007/978-3-030-72240-1_62
Download citation
DOI: https://doi.org/10.1007/978-3-030-72240-1_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-72239-5
Online ISBN: 978-3-030-72240-1
eBook Packages: Computer ScienceComputer Science (R0)