A Novel Framework for Multi-Document Temporal Summarization (MDTS)

Kishore Kumar Mamidala, Suresh Kumar Sanampudi


Internet or Web consists of a massive amount of information, handling which is a tedious task. Summarization plays a crucial role in extracting or abstracting key content from multiple sources with its meaning contained, thereby reducing the complexity in handling the information. Multi-document summarization gives the gist of the content collected from multiple documents. Temporal summarization concentrates on temporally related events. This paper proposes a Multi-Document Temporal Summarization (MDTS) technique that generates the summary based on temporally related events extracted from multiple documents. This technique extracts the events with the time stamp. TIMEML standards tags are used in extracting events and times. These event-times are stored in a structured database form for easier operations. Sentence ranking methods are build based on the frequency of events occurrences in the sentence. Sentence similarity measures are computed to eliminate the redundant sentences in an extracted summary. Depending on the required summary length, top-ranked sentences are selected to form the summary. Experiments are conducted on DUC 2006 and DUC 2007 data set that was released for multi-document summarization task. The extracted summaries are evaluated using ROUGE to determine precision, recall and F measure of generated summaries. The performance of the proposed method is compared with particle swarm optimization-based algorithm (PSOS), Cat swarm optimization-based summarization (CSOS), Cuckoo Search based multi-document summarization (MDSCSA). It is found that the performance of MDTS is better when compared with other methods.


Doi: 10.28991/esj-2021-01268

Full Text: PDF


Natural Language; Processing; Text Summarization; Multi-document Summarization; Extractive Summary; Temporal Summarization.


Zhang, Chunyun, Weiyan Xu, Fanyu Meng, Hongyan Li, Tong Wu, and Lixin Xu. "The Information Extraction Systems of PRIS at Temporal Summarization Track." In TREC. (2013).

Marcu, Daniel. "Discourse trees are good indicators of importance in text." Advances in automatic text summarization 293 (1999): 123-136.

Filatova, Elena, and Eduard Hovy. "Assigning time-stamps to event-clauses." In Proceedings of the ACL 2001 Workshop on Temporal and Spatial Information Processing. (2001):1-8.

Mani, Inderjeet, and Mark T. Maybury. "Advances in automatic text summarization, vol. 293." Camb MA (1999).

James Pustejovsky,Jessica Littman,Robert Knippen, and Roser Sauri:‟Temporal and Event Information in Natural Language Text, Language Resources and Evaluation‟, 2005, 39(2-3):123-164.

Pustejovsky, James, Robert Knippen, Jessica Littman, and Roser Saurí. “Temporal and Event Information in Natural Language Text.” Language Resources and Evaluation 39, no. 2–3 (May 2005): 123–164. doi:10.1007/s10579-005-7882-7.

Färber, M., and Adam J. Finding Temporal Trends of Scientific Concepts. In Proceedings of the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019) (CEUR Workshop Proceedings), Vol. 2345. (2019):132-139.

Grüninger, Michael, and Zhuojun Li. "The time ontology of Allen's interval algebra." In 24th International Symposium on Temporal Representation and Reasoning (TIME 2017). Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, (2017):1-16.

Mohd Pozi, Muhammad Syafiq, Adam Jatowt, and Yukiko Kawai. “Temporal Summarization of Scholarly Paper Collections by Semantic Change Estimation: Case Study of CORD-19 Dataset.” Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (August 2020). doi:10.1145/3383583.3398563.

Pustejovsky, James, Robert Knippen, Jessica Littman, and Roser Saurí. “Temporal and Event Information in Natural Language Text.” Language Resources and Evaluation 39, no. 2–3 (May 2005): 123–164. doi:10.1007/s10579-005-7882-7.

Guo, Qi, Fernando Diaz, and Elad Yom-Tov. “Updating Users About Time Critical Events.” Advances in Information Retrieval (2013): 483–494. doi:10.1007/978-3-642-36973-5_41.

Rautray, Rasmita, and Rakesh Chandra Balabantaray. “An Evolutionary Framework for Multi Document Summarization Using Cuckoo Search Approach: MDSCSA.” Applied Computing and Informatics 14, no. 2 (July 2018): 134–144. doi:10.1016/j.aci.2017.05.003.

Alguliev, Rasim M., Ramiz M. Aliguliyev, Makrufa S. Hajirahimova, and Chingiz A. Mehdiyev. “MCMR: Maximum Coverage and Minimum Redundant Text Summarization Model.” Expert Systems with Applications 38, no. 12 (November 2011): 14514–14522. doi:10.1016/j.eswa.2011.05.033.

Roser Saur ́ı, Jessica Littman, Bob Knippen, Robert Gaizauskas, Andrea Setzer, and James Pustejovsky: TimeML Annotation Guidelines: Version 1.2.1, January 31, (2006).

Harabagiu, Sanda M., and Finley Lacatusu. "Generating single and multi-document summaries with gistexter." In Document Understanding Conferences, (2002): 40-45.

Sanampudi, Suresh Kumar, and G.Vijaya Kumari. “Temporal Reasoning in Natural Language Processing: A Survey.” International Journal of Computer Applications 1, no. 4 (February 25, 2010): 68–72. doi:10.5120/100-209.

Guda, Vanitha, and SureshKumar Sanampudi. “Event Time Relationship in Natural Language Text.” International Journal of Recent Contributions from Engineering, Science & IT (iJES) 7, no. 3 (September 25, 2019): 4. doi:10.3991/ijes.v7i3.10985.

Li, Wei, Xinyan Xiao, Jiachen Liu, Hua Wu, Haifeng Wang, and Junping Du. "Leveraging Graph to Improve Abstractive Multi-Document Summarization." arXiv preprint arXiv:2005.10043 (2020).

Liu, Yang, and Mirella Lapata. "Hierarchical transformers for multi-document summarization." arXiv preprint arXiv:1905.13164 (2019).

Yu N, Huang M, Shi Y, zhu x, Product re-view summarization by exploiting phrase proper-ties. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, The COLING 2016 Organizing Committee, Osaka, Japan, (2016):1113–1124.

Zhang, Yang, Yunqing Xia, Yi Liu, and Wenmin Wang. “Clustering Sentences with Density Peaks for Multi-Document Summarization.” Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2015). doi:10.3115/v1/n15-1136.

Ng, Jun-Ping, Praveen Bysani, Ziheng Lin, Min-Yen Kan, and Chew Lim Tan. "Exploiting Category-Specific Information for Multi-Document Summarization." In COLING, (2012): 2093-2108.

Full Text: PDF

DOI: 10.28991/esj-2021-01268


  • There are currently no refbacks.