{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T03:00:14Z","timestamp":1780974014745,"version":"3.54.1"},"reference-count":67,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003459","name":"Guizhou University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3304500"],"award-info":[{"award-number":["2023YFC3304500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132143","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T15:41:47Z","timestamp":1774021307000},"page":"132143","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A causality-aware and hallucination-mitigation framework for faithful news summarization"],"prefix":"10.1016","volume":"319","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9313-4718","authenticated-orcid":false,"given":"Caiwei","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4195-5522","authenticated-orcid":false,"given":"Shuai","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9946-3157","authenticated-orcid":false,"given":"Yanping","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8941-3914","authenticated-orcid":false,"given":"Ruizhang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3631-9721","authenticated-orcid":false,"given":"Yongbin","family":"Qin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3714-5257","authenticated-orcid":false,"given":"Chuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132143_bib0001","unstructured":"Abdin, M., Aneja, J., Behl, H., Bubeck, S., Eldan, R., Gunasekar, S., Harrison, M., Hewett, R.J., Javaheripi, M., Kauffmann, P. et al. (2024). Phi-4 technical report. arXiv: 2412.08905."},{"key":"10.1016\/j.eswa.2026.132143_bib0002","series-title":"Generative AI: Current trends and applications","first-page":"147","article-title":"Comprehensive analysis of falcon 7b: A state-of-the-art generative large language model","author":"Aridoss","year":"2024"},{"key":"10.1016\/j.eswa.2026.132143_bib0003","doi-asserted-by":"crossref","unstructured":"Bhandari, M., Gour, P., Ashfaq, A., Liu, P., & Neubig, G. (2020). Re-evaluating evaluation in text summarization. arXiv: 2010.07100,.","DOI":"10.18653\/v1\/2020.emnlp-main.751"},{"key":"10.1016\/j.eswa.2026.132143_bib0004","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"549","article-title":"Rumor detection on social media with bi-directional graph convolutional networks","volume":"vol. 34","author":"Bian","year":"2020"},{"key":"10.1016\/j.eswa.2026.132143_bib0005","series-title":"Proceedings of the 2021 conference on empirical methods in natural language processing","first-page":"6633","article-title":"CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization","author":"Cao","year":"2021"},{"key":"10.1016\/j.eswa.2026.132143_bib0006","series-title":"Proceedings of the 20th international conference on world wide web","first-page":"675","article-title":"Information credibility on twitter","author":"Castillo","year":"2011"},{"key":"10.1016\/j.eswa.2026.132143_bib0007","series-title":"Proceedings of the 2019 IEEE\/ACM international conference on advances in social networks analysis and mining","first-page":"41","article-title":"Same: sentiment-aware multi-modal embedding for detecting fake news","author":"Cui","year":"2019"},{"issue":"7","key":"10.1016\/j.eswa.2026.132143_bib0008","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0321919","article-title":"Semantic knowledge graph fusion for fake news detection: Unifying content-based features and evidence-based analysis in the COVID-19 infodemic","volume":"20","author":"Dar","year":"2025","journal-title":"PloS One"},{"key":"10.1016\/j.eswa.2026.132143_bib0009","series-title":"Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers)","first-page":"4171","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2019"},{"key":"10.1016\/j.eswa.2026.132143_bib0010","series-title":"Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies","first-page":"4830","article-title":"GSUM: A general framework for guided neural abstractive summarization","author":"Dou","year":"2021"},{"issue":"3","key":"10.1016\/j.eswa.2026.132143_bib0011","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1017\/S135132492100036X","article-title":"A survey of the extraction and applications of causal relations","volume":"28","author":"Drury","year":"2022","journal-title":"Natural Language Engineering"},{"key":"10.1016\/j.eswa.2026.132143_bib0012","unstructured":"Du, Z., Qian, Y., Liu, X., Ding, M., Qiu, J., Yang, Z., & Tang, J. (2021). GLM: General language model pretraining with autoregressive blank infilling. arXiv: 2103.10360."},{"key":"10.1016\/j.eswa.2026.132143_bib0013","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1162\/tacl_a_00373","article-title":"SummEval: Re-evaluating summarization evaluation","volume":"9","author":"Fabbri","year":"2021","journal-title":"Transactions of the Association for Computational Linguistics"},{"key":"10.1016\/j.eswa.2026.132143_bib0014","series-title":"A systematic survey of relation extraction for knowledge graph question answering","author":"Galstyan","year":"2022"},{"key":"10.1016\/j.eswa.2026.132143_bib0015","series-title":"Companion proceedings of the ACM on web conference 2025","first-page":"981","article-title":"Unseen fake news detection through casual debiasing","author":"Gong","year":"2025"},{"key":"10.1016\/j.eswa.2026.132143_bib0016","doi-asserted-by":"crossref","unstructured":"Goyal, T., & Durrett, G. (2021). Annotating and modeling fine-grained factuality in summarization. arXiv: 2104.04302.","DOI":"10.18653\/v1\/2021.naacl-main.114"},{"issue":"12","key":"10.1016\/j.eswa.2026.132143_bib0017","doi-asserted-by":"crossref","first-page":"15486","DOI":"10.1109\/TITS.2022.3185013","article-title":"Mixed graph neural network-based fake news detection for sustainable vehicular social networks","volume":"24","author":"Guo","year":"2022","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.132143_bib0018","unstructured":"Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., & Sun, M. (2019). OpenNRE: An open and extensible toolkit for neural relation extraction. arXiv: 1909.13078."},{"key":"10.1016\/j.eswa.2026.132143_bib0019","series-title":"Findings of the association for computational linguistics: ACL 2022","first-page":"3307","article-title":"Comparative opinion summarization via collaborative decoding","author":"Iso","year":"2022"},{"key":"10.1016\/j.eswa.2026.132143_bib0020","unstructured":"Kry\u015bci\u0144ski, W., McCann, B., Xiong, C., & Socher, R. (2019). Evaluating the factual consistency of abstractive text summarization. arXiv: 1910.12840."},{"key":"10.1016\/j.eswa.2026.132143_bib0021","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1162\/tacl_a_00453","article-title":"SummaC: Re-visiting NLI-based models for inconsistency detection in summarization","volume":"10","author":"Laban","year":"2022","journal-title":"Transactions of the Association for Computational Linguistics"},{"issue":"9","key":"10.1016\/j.eswa.2026.132143_bib0022","doi-asserted-by":"crossref","DOI":"10.1061\/JBENF2.BEENG-6159","article-title":"Sustainable life-cycle maintenance policymaking for network-level deteriorating bridges with a convolutional autoencoder\u2013structured reinforcement learning agent","volume":"28","author":"Lei","year":"2023","journal-title":"Journal of Bridge Engineering"},{"key":"10.1016\/j.eswa.2026.132143_bib0023","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2019). Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv: 1910.13461.","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"10.1016\/j.eswa.2026.132143_bib0024","first-page":"9459","article-title":"RetriEval-augmented generation for knowledge-intensive nlp tasks","volume":"33","author":"Lewis","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132143_bib0025","series-title":"Text summarization branches out","first-page":"74","article-title":"Rouge: A package for automatic evaluation of summaries","author":"Lin","year":"2004"},{"key":"10.1016\/j.eswa.2026.132143_bib0026","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2769","article-title":"Graph neural networks embedded with domain knowledge for cyber threat intelligence entity and relationship mining","volume":"11","author":"Liu","year":"2025","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.eswa.2026.132143_bib0027","doi-asserted-by":"crossref","unstructured":"Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020). On faithfulness and factuality in abstractive summarization. arXiv: 2005.00661.","DOI":"10.18653\/v1\/2020.acl-main.173"},{"key":"10.1016\/j.eswa.2026.132143_bib0028","doi-asserted-by":"crossref","unstructured":"Miranda, S., Znoti\u0146\u0161, A., Cohen, S.B., & Barzdins, G. (2018). Multilingual clustering of streaming news. arXiv: 1809.00540.","DOI":"10.18653\/v1\/D18-1483"},{"key":"10.1016\/j.eswa.2026.132143_bib0029","doi-asserted-by":"crossref","unstructured":"Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B. et al. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv: 1602.06023.","DOI":"10.18653\/v1\/K16-1028"},{"key":"10.1016\/j.eswa.2026.132143_bib0030","doi-asserted-by":"crossref","unstructured":"Nan, F., Nallapati, R., Wang, Z., Santos, C. N. d., Zhu, H., Zhang, D., McKeown, K., & Xiang, B. (2021). Entity-level factual consistency of abstractive text summarization. arXiv: 2102.09130.","DOI":"10.18653\/v1\/2021.eacl-main.235"},{"key":"10.1016\/j.eswa.2026.132143_bib0031","unstructured":"Nenkova, A. (2005). Automatic text summarization of newswire: Lessons learned from the document understanding conference."},{"issue":"2\u20133","key":"10.1016\/j.eswa.2026.132143_bib0032","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1561\/1500000015","article-title":"Automatic summarization","volume":"5","author":"Nenkova","year":"2011","journal-title":"Foundations and Trends\u00ae in Information Retrieval"},{"key":"10.1016\/j.eswa.2026.132143_bib0033","unstructured":"Ouyang, Y., Wu, P., & Pan, L. (2024). Cool: Comprehensive knowledge enhanced prompt learning for domain adaptive few-shot fake news detection. arXiv: 2406.10870."},{"key":"10.1016\/j.eswa.2026.132143_bib0034","series-title":"Proceedings of the 40th annual meeting of the association for computational linguistics","first-page":"311","article-title":"Bleu: a method for automatic evaluation of machine translation","author":"Papineni","year":"2002"},{"key":"10.1016\/j.eswa.2026.132143_bib0035","unstructured":"Potthast, M., Kiesel, J., Reinartz, K., Bevendorff, J., & Stein, B. (2017). A stylometric inquiry into hyperpartisan and fake news. arXiv: 1702.05638."},{"key":"10.1016\/j.eswa.2026.132143_bib0036","series-title":"International conference on image and graphics","first-page":"68","article-title":"Causality-inspired source-free domain adaptation for medical image classification","author":"Qiu","year":"2023"},{"issue":"140","key":"10.1016\/j.eswa.2026.132143_bib0037","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.132143_bib0038","series-title":"Proceedings of the 2017 conference on empirical methods in natural language processing","first-page":"2931","article-title":"Truth of varying shades: Analyzing language in fake news and political fact-checking","author":"Rashkin","year":"2017"},{"issue":"1","key":"10.1016\/j.eswa.2026.132143_bib0039","article-title":"A survey on automatic twitter event summarization","volume":"14","author":"Rudrapal","year":"2018","journal-title":"Journal of Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132143_bib0040","unstructured":"See, A., Liu, P.J., & Manning, C.D. (2017). Get to the point: Summarization with pointer-generator networks. arXiv: 1704.04368."},{"issue":"5","key":"10.1016\/j.eswa.2026.132143_bib0041","doi-asserted-by":"crossref","first-page":"269","DOI":"10.23940\/ijpe.25.05.p4.269277","article-title":"Causality extraction and reasoning from text","volume":"21","author":"Sharma","year":"2025","journal-title":"International Journal of Performability Engineering"},{"issue":"3","key":"10.1016\/j.eswa.2026.132143_bib0042","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1089\/big.2020.0062","article-title":"FakeNewsNet: A data repository with news content, social context, and spatiotemporal information for studying fake news on social media","volume":"8","author":"Shu","year":"2020","journal-title":"Big Data"},{"issue":"1","key":"10.1016\/j.eswa.2026.132143_bib0043","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/3137597.3137600","article-title":"Fake news detection on social media: A data mining perspective","volume":"19","author":"Shu","year":"2017","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"10.1016\/j.eswa.2026.132143_bib0044","unstructured":"G. Team, Riviere, M., Pathak, S., Sessa, P.G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ram\u00e9, A. et al. (2024a). Gemma 2: Improving open language models at a practical size. arXiv: 2408.00118."},{"key":"10.1016\/j.eswa.2026.132143_bib0045","unstructured":"Q. Team et al. (2024b). Qwen2 technical report. arXiv: 2407.10671, 2, 3."},{"key":"10.1016\/j.eswa.2026.132143_bib0046","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., Azhar, F. et al. (2023). Llama: Open and efficient foundation language models. arXiv: 2302.13971."},{"key":"10.1016\/j.eswa.2026.132143_bib0047","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132143_bib0048","series-title":"Proceedings of the 2022 conference of the North American chapter of the association for computational linguistics: Human language technologies","first-page":"1010","article-title":"FactPEGASUS: Factuality-aware pre-training and fine-tuning for abstractive summarization","author":"Wan","year":"2022"},{"key":"10.1016\/j.eswa.2026.132143_bib0049","doi-asserted-by":"crossref","unstructured":"Wang, N., Han, X., Singh, J., Ma, J., & Chaudhary, V. (2025a). Causalrag: Integrating causal graphs into retrieval-augmented generation. arXiv: 2503.19878.","DOI":"10.18653\/v1\/2025.findings-acl.1165"},{"key":"10.1016\/j.eswa.2026.132143_bib0050","series-title":"Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining","first-page":"849","article-title":"EANN: Event adversarial neural networks for multi-modal fake news detection","author":"Wang","year":"2018"},{"key":"10.1016\/j.eswa.2026.132143_bib0051","doi-asserted-by":"crossref","unstructured":"Wang, Z., Sheng, Q., Wang, D., Hu, B., & Cao, J. (2025b). Bridging thoughts and words: Graph-based intent-semantic joint learning for fake news detection. arXiv: 2509.01660.","DOI":"10.1145\/3746252.3761400"},{"key":"10.1016\/j.eswa.2026.132143_bib0052","series-title":"Proceedings of the 31st international conference on computational linguistics","first-page":"9407","article-title":"Cross-domain fake news detection based on dual-granularity adversarial training","author":"Wei","year":"2025"},{"key":"10.1016\/j.eswa.2026.132143_bib0053","unstructured":"Wu, A., Kuang, K., Zhu, M., Wang, Y., Zheng, Y., Han, K., Li, B., Chen, G., Wu, F., & Zhang, K. (2024). Causality for large language models. arXiv: 2410.15319."},{"key":"10.1016\/j.eswa.2026.132143_bib0054","unstructured":"Xiao, Y., Dong, J., Zhou, C., Dong, S., Zhang, Q.-w., Yin, D., Sun, X., & Huang, X. (2025). GraphRAG-bench: Challenging domain-specific reasoning for evaluating graph retrieval-augmented generation. arXiv: 2506.02404."},{"key":"10.1016\/j.eswa.2026.132143_bib0055","series-title":"Proceedings of the ACM web conference 2022","first-page":"2501","article-title":"Evidence-aware fake news detection with graph neural networks","author":"Xu","year":"2022"},{"key":"10.1016\/j.eswa.2026.132143_bib0056","unstructured":"Yang, A., Xiao, B., Wang, B., Zhang, B., Bian, C., Yin, C., Lv, C., Pan, D., Wang, D., Yan, D. et al. (2023). Baichuan 2: Open large-scale language models. arXiv: 2309.10305."},{"issue":"5","key":"10.1016\/j.eswa.2026.132143_bib0057","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1007\/s10115-022-01665-w","article-title":"A survey on extraction of causal relations from natural language text","volume":"64","author":"Yang","year":"2022","journal-title":"Knowledge and Information Systems"},{"key":"10.1016\/j.eswa.2026.132143_bib0058","unstructured":"Yao, L., Mao, C., & Luo, Y. (2019). KG-BERT: Bert for knowledge graph completion. arXiv: 1909.03193."},{"key":"10.1016\/j.eswa.2026.132143_bib0059","unstructured":"Young, A., Chen, B., Li, C., Huang, C., Zhang, G., Zhang, G., Wang, G., Li, H., Zhu, J., Chen, J. et al. (2024). YI: Open foundation models by 01. AI. arXiv: 2403.04652."},{"issue":"1","key":"10.1016\/j.eswa.2026.132143_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2024.103918","article-title":"IterSum: Iterative summarization based on document topological structure","volume":"62","author":"Yu","year":"2025","journal-title":"Information Processing & Management"},{"key":"10.1016\/j.eswa.2026.132143_bib0061","first-page":"27263","article-title":"BartScore: Evaluating generated text as text generation","volume":"34","author":"Yuan","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10.1016\/j.eswa.2026.132143_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2025.106182","article-title":"Enhancing bridge inspection data quality using machine learning","volume":"175","author":"Zhang","year":"2025","journal-title":"Automation in Construction"},{"key":"10.1016\/j.eswa.2026.132143_bib0063","series-title":"From Text to Knowledge: A New Approach to REF2014 Impact Case Study Analytics","author":"Zhang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132143_bib0064","series-title":"International conference on machine learning","first-page":"11328","article-title":"PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization","author":"Zhang","year":"2020"},{"key":"10.1016\/j.eswa.2026.132143_bib0065","unstructured":"Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2019). BertScore: Evaluating text generation with bert. arXiv: 1904.09675."},{"issue":"2","key":"10.1016\/j.eswa.2026.132143_bib0066","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1145\/3373464.3373473","article-title":"Network-based fake news detection: A pattern-driven approach","volume":"21","author":"Zhou","year":"2019","journal-title":"ACM SIGKDD Explorations Newsletter"},{"issue":"5","key":"10.1016\/j.eswa.2026.132143_bib0067","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3395046","article-title":"A survey of fake news: Fundamental theories, detection methods, and opportunities","volume":"53","author":"Zhou","year":"2020","journal-title":"ACM Computing Surveys (CSUR)"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010560?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426010560?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T02:46:52Z","timestamp":1780973212000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426010560"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":67,"alternative-id":["S0957417426010560"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132143","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A causality-aware and hallucination-mitigation framework for faithful news summarization","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132143","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132143"}}