Based on the distributed RDF dataset statistics, more than 10.000 datasets are following Semantic Web standards. It provides a billion triples (RDF data) that are used by many applications to provide information to the human. Avoiding information overload on the data consumers [1] and making it more concise and recapitulative [2] are some reasons used by researchers to conduct research on Entity Summarization. Entity summarization is a technique to establish short summary (of) RDF data [2][3] so that users are able to identify the underlying identity quickly [3]. Comparing to document summarization where the data is unstructured and there are many frequent words, entity summarization works on RDF that is structured and without frequent terms. Many researchers have been conducted researches on individual entity to produce entity summaries. Generally, there are two categories of entity summarization methods, namely, supervised learning and unsupervised learning.
2021
Entity Summarization: State of the Art and Future Challenges
Paper: https://www.sciencedirect.com/science/article/abs/pii/S1570826821000226
2010
DIVERSUM: Towards diversified summarisation of entities in knowledge graphs
Paper: https://sci-hub.tw/10.1109/ICDEW.2010.5452707
2011
RELIN: RELatedness and Informativeness-based centrality for entity summarizatioN [3]
Paper: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.352.7610&rep=rep1&type=pdf
2015
FACES: diversity-aware entity summarization using incremental hierarchical conceptual clustering
Paper: https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9562/9233
2016
Linksum: Using link analysis to summarize entity data [6]
Paper: https://www.aifb.kit.edu/images/4/43/LinkSUM.pdf
2017
Dynamic Factual Summaries for Entity Cards
Code: https://github.com/iai-group/DynamicEntitySummarization-DynES
Paper: https://dl.acm.org/doi/10.1145/3077136.3080810
ESLDA: entity summarization using knowledge-based topic modeling
Paper: https://www.aclweb.org/anthology/I17-1032.pdf
Semantics-based Entity Summarization
Paper: https://kalpagunaratna.github.io/swsa_files/swsa_short_paper-Kalpa.pdf
2018
Entity Summarization Based on Formal Concept Analysis
Code: https://github.com/kekeeo/KAFCA
Paper: http://semanticweb.kaist.ac.kr/home/images/c/cb/Entity_Summarization_Based_on_Formal_Concept_Analysis.pdf
BAFREC: Balancing Frequency and Rarity for Entity Characterization in Open Linked Data
Paper: http://www.ifis.cs.tu-bs.de/sites/default/files/eyre2018-kroll-BAFREC-camera-ready.pdf
Combining Word Embedding and Knowledge-Based Topic Modeling for Entity Summarization
Paper: https://ieeexplore.ieee.org/document/8334467
MPSUM: Entity Summarization with Predicate-based Matching
Code: https://github.com/WeiDongjunGabriel/MPSUM.git
Paper: https://arxiv.org/abs/2005.11992
PageRank and Generic EntitySummarization for RDF Knowledge Bases
Paper: https://link.springer.com/chapter/10.1007/978-3-319-93417-4_10
2020
Entity Summarization in Fuzzy Knowledge Graph Based on Fuzzy Concept Analysis
Paper: https://link.springer.com/chapter/10.1007/978-981-15-9309-3_3
2021
Incremental Entity Summarization with Formal Concept Analysis
Paper: https://ieeexplore.ieee.org/document/9459533
Entity Summarization in Fuzzy Knowledge Graph Based on Fuzzy Concept Analysis
Paper: https://link.springer.com/chapter/10.1007%2F978-981-15-9309-3_3
2019
ESA:Entity Summarization Attention [2]
Code: https://github.com/WeiDongjunGabriel/ESA
Paper: https://arxiv.org/abs/1905.10625
2020
DeepLENS: Deep Learning for Entity Summarization
Code: https://github.com/nju-websoft/DeepLENS
Paper: https://arxiv.org/abs/2003.03736
Entity Summarization with User Feedback
Code: https://github.com/nju-websoft/DRESSED
Paper: https://link.springer.com/content/pdf/10.1007%2F978-3-030-49461-2.pdf
Neural Entity Summarization with Joint Encoding and Weak Supervision
Code: https://github.com/lijunyou/IJCAI2020
Paper: https://www.ijcai.org/Proceedings/2020/0228.pdf
AutoSUM: Automating Feature Extraction and Multi-user Preference for Entity Summarization
Code: https://github.com/WeiDongjunGabriel/AutoSUM
Paper: https://link.springer.com/chapter/10.1007%2F978-3-030-47436-2_44
2021
GATES: Graph Attention Networks for Entity Summarization
Code: https://github.com/dice-group/GATES
Paper: https://dl.acm.org/doi/10.1145/3460210.3493574
2023
ESCS: Entity Summarization via Exploiting Description Complementary and Salience
Paper: https://ieeexplore.ieee.org/document/9718581
2024
ESLM: Improving Entity Summarization by Leveraging Language Models
Code: https://github.com/dice-group/ESLM
Paper: https://papers.dice-research.org/2024/ESWC_ESLM/public.pdf
2020 Paper: https://link.springer.com/chapter/10.1007/978-3-030-49461-2_32
Code: https://github.com/nju-websoft/ESBM/tree/master/v1.1
Code: https://github.com/nju-websoft/ESBM/tree/master/v1.2
Original source: http://wiki.knoesis.org/index.php/FACES
Backup of original source: https://files.dice-research.org/users/asep/datasets/entity-summarization/original_faces_dataset.zip
Source: https://github.com/dice-group/GATES/tree/main/data/FACES
2024
Paper: https://arxiv.org/abs/2406.08435
Code: https://github.com/msorkhpar/wiki-entity-summarization
Toolkit: https://github.com/msorkhpar/wiki-entity-summarization-toolkit
2017
REMES: Relatedness-based multi entity summarization
Paper: https://www.ijcai.org/Proceedings/2017/147
Micro-review synthesis for multi-entity summarization
Paper: https://link.springer.com/article/10.1007/s10618-017-0491-4
Using Pre-trained Language Models for Abstractive DBPEDIA Summarization: A Comparative Study
Paper: https://papers.dice-research.org/2023/SEMANTICS_LLM_DBpedia/main_public.pdf
Resources: https://zenodo.org/records/7441120
2020
VISION-KG: Topic-centric Visualization System for Summarizing Knowledge Graph
Paper: https://dl.acm.org/doi/pdf/10.1145/3336191.3371863
2024
A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
Paper:https://arxiv.org/abs/2402.12001
[1] Gunaratna, K., Yazdavar, A. H., Thirunarayan, K., Sheth, A., & Cheng, G. (2017, August). Relatedness-based multi-entity summarization. In IJCAI: proceedings of the conference (Vol. 2017, p. 1060). NIH Public Access.
[2] Wei, D., & Liu, Y. (2019). ESA: Entity Summarization with Attention. arXiv preprint arXiv:1905.10625.
[3] Cheng, G., Tran, T., & Qu, Y. (2011, October). Relin: relatedness and informativeness-based centrality for entity summarization. In International Semantic Web Conference (pp. 114-129). Springer, Berlin, Heidelberg.
[4] Liu, Q., Cheng, G., Gunaratna, K., & Qu, Y. (2019). Entity Summarization: State of the Art and Future Challenges. arXiv preprint arXiv:1910.08252.
[5] Gunaratna, K., Thirunarayan, K., & Sheth, A. (2015, February). FACES: diversity-aware entity summarization using incremental hierarchical conceptual clustering. In Twenty-Ninth AAAI Conference on Artificial Intelligence.
[6] Thalhammer, A., Lasierra, N., & Rettinger, A. (2016, June). Linksum: using link analysis to summarize entity data. In International Conference on Web Engineering (pp. 244-261). Springer, Cham.