{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:08:23Z","timestamp":1765544903699,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T00:00:00Z","timestamp":1689638400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Advancing Theoretical Minimax Deep Learning: Optimization, Resilience, and Interpretability.","award":["NSF DMS-2134223"],"award-info":[{"award-number":["NSF DMS-2134223"]}]},{"name":"SCH: Geometry and Topology for Interpretable and Reliable Deep Learning in Medical Imaging","award":["NSF IIS-2205418"],"award-info":[{"award-number":["NSF IIS-2205418"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,7,19]]},"DOI":"10.1145\/3539618.3591936","type":"proceedings-article","created":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T00:22:59Z","timestamp":1689726179000},"page":"1745-1749","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Lightweight Constrained Generation Alternative for Query-focused Summarization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2370-4487","authenticated-orcid":false,"given":"Zhichao","family":"Xu","sequence":"first","affiliation":[{"name":"University of Utah, Salt Lake City, UT, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2724-8247","authenticated-orcid":false,"given":"Daniel","family":"Cohen","sequence":"additional","affiliation":[{"name":"Dataminr, Inc., New York City, NY, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Guided open vocabulary image captioning with constrained beam search. arXiv preprint arXiv:1612.00576","author":"Anderson Peter","year":"2016","unstructured":"Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016. Guided open vocabulary image captioning with constrained beam search. arXiv preprint arXiv:1612.00576 (2016)."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2590972"},{"key":"e_1_3_2_2_3_1","volume-title":"Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding. arXiv preprint arXiv:2207.01262","author":"Boytsov Leonid","year":"2022","unstructured":"Leonid Boytsov, Tianyi Lin, Fangwei Gao, Yutian Zhao, Jeffrey Huang, and Eric Nyberg. 2022. Understanding Performance of Long-Document Ranking Models through Comprehensive Evaluation and Leaderboarding. arXiv preprint arXiv:2207.01262 (2022)."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380206"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.3115\/1654679.1654689"},{"key":"e_1_3_2_2_6_1","volume-title":"Multi-hop inference for question-driven summarization. arXiv preprint arXiv:2010.03738","author":"Deng Yang","year":"2020","unstructured":"Yang Deng, Wenxuan Zhang, and Wai Lam. 2020. Multi-hop inference for question-driven summarization. arXiv preprint arXiv:2010.03738 (2020)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1.12918"},{"key":"e_1_3_2_2_8_1","volume-title":"Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber.","author":"Feng Shi","year":"2018","unstructured":"Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber. 2018. Pathologies of neural models make interpretations difficult. arXiv preprint arXiv:1804.07781 (2018)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72240-1_26"},{"key":"e_1_3_2_2_10_1","volume-title":"Lexically constrained decoding for sequence generation using grid beam search. arXiv preprint arXiv:1704.07138","author":"Hokamp Chris","year":"2017","unstructured":"Chris Hokamp and Qun Liu. 2017. Lexically constrained decoding for sequence generation using grid beam search. arXiv preprint arXiv:1704.07138 (2017)."},{"key":"e_1_3_2_2_11_1","volume-title":"Survey of hallucination in natural language generation. Comput. Surveys","author":"Ji Ziwei","year":"2022","unstructured":"Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2022. Survey of hallucination in natural language generation. Comput. Surveys (2022)."},{"key":"e_1_3_2_2_12_1","volume-title":"Pubmedqa: A dataset for biomedical research question answering. arXiv preprint arXiv:1909.06146","author":"Jin Qiao","year":"2019","unstructured":"Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W Cohen, and Xinghua Lu. 2019. Pubmedqa: A dataset for biomedical research question answering. arXiv preprint arXiv:1909.06146 (2019)."},{"key":"e_1_3_2_2_13_1","volume-title":"Canadian conference on artificial intelligence. Springer, 342--348","author":"Rahman Laskar Md Tahmid","year":"2020","unstructured":"Md Tahmid Rahman Laskar, Enamul Hoque, and Jimmy Huang. 2020. Query focused abstractive summarization via incorporating query relevance and transfer learning with transformer models. In Canadian conference on artificial intelligence. Springer, 342--348."},{"key":"e_1_3_2_2_14_1","volume-title":"Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461","author":"Lewis Mike","year":"2019","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019. Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)."},{"key":"e_1_3_2_2_15_1","volume-title":"Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74--81.","author":"Lin Chin-Yew","year":"2004","unstructured":"Chin-Yew Lin. 2004. Rouge: A package for automatic evaluation of summaries. In Text summarization branches out. 74--81."},{"key":"e_1_3_2_2_16_1","volume-title":"Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"e_1_3_2_2_17_1","volume-title":"Lianhui Qin, Youngjae Yu, Rowan Zellers, et al.","author":"Lu Ximing","year":"2021","unstructured":"Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, et al. 2021. Neurologic a* esque decoding: Constrained text generation with lookahead heuristics. arXiv preprint arXiv:2112.08726 (2021)."},{"key":"e_1_3_2_2_18_1","volume-title":"Chandra Bhagavatula, and Yejin Choi.","author":"Lu Ximing","year":"2020","unstructured":"Ximing Lu, Peter West, Rowan Zellers, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2020. Neurologic decoding:(un) supervised neural text generation with predicate logic constraints. arXiv preprint arXiv:2010.12884 (2020)."},{"key":"e_1_3_2_2_19_1","volume-title":"On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661","author":"Maynez Joshua","year":"2020","unstructured":"Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661 (2020)."},{"key":"e_1_3_2_2_20_1","volume-title":"Diversity driven attention model for query-based abstractive summarization. arXiv preprint arXiv:1704.08300","author":"Nema Preksha","year":"2017","unstructured":"Preksha Nema, Mitesh Khapra, Anirban Laha, and Balaraman Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. arXiv preprint arXiv:1704.08300 (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"MS MARCO: A human generated machine reading comprehension dataset. In CoCo@ NIPs.","author":"Nguyen Tri","year":"2016","unstructured":"Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. In CoCo@ NIPs."},{"key":"e_1_3_2_2_22_1","volume-title":"Document ranking with a pretrained sequence-to-sequence model. arXiv preprint arXiv:2003.06713","author":"Nogueira Rodrigo","year":"2020","unstructured":"Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2020. Document ranking with a pretrained sequence-to-sequence model. arXiv preprint arXiv:2003.06713 (2020)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531901"},{"key":"e_1_3_2_2_24_1","first-page":"1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu, et al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res., Vol. 21, 140 (2020), 1--67.","journal-title":"J. Mach. Learn. Res."},{"key":"e_1_3_2_2_25_1","volume-title":"Explaining nlp models via minimal contrastive editing (mice). arXiv preprint arXiv:2012.13985","author":"Ross Alexis","year":"2020","unstructured":"Alexis Ross, Ana Marasovi\u0107, and Matthew E Peters. 2020. Explaining nlp models via minimal contrastive editing (mice). arXiv preprint arXiv:2012.13985 (2020)."},{"key":"e_1_3_2_2_26_1","first-page":"4","article-title":"Towards extracting faithful and descriptive representations of latent variable models. AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR)","volume":"1","author":"Sanchez Ivan","year":"2015","unstructured":"Ivan Sanchez, Tim Rocktaschel, Sebastian Riedel, and Sameer Singh. 2015. Towards extracting faithful and descriptive representations of latent variable models. AAAI Spring Syposium on Knowledge Representation and Reasoning (KRR): Integrating Symbolic and Neural Approaches, Vol. 1 (2015), 4--1.","journal-title":"Integrating Symbolic and Neural Approaches"},{"key":"e_1_3_2_2_27_1","volume-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034","author":"Simonyan Karen","year":"2013","unstructured":"Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2013. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034 (2013)."},{"key":"e_1_3_2_2_28_1","volume-title":"Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825","author":"Smilkov Daniel","year":"2017","unstructured":"Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Vi\u00e9gas, and Martin Wattenberg. 2017. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)."},{"key":"e_1_3_2_2_29_1","volume-title":"Improve query focused abstractive summarization by incorporating answer relevance. arXiv preprint arXiv:2105.12969","author":"Su Dan","year":"2021","unstructured":"Dan Su, Tiezheng Yu, and Pascale Fung. 2021. Improve query focused abstractive summarization by incorporating answer relevance. arXiv preprint arXiv:2105.12969 (2021)."},{"key":"e_1_3_2_2_30_1","volume-title":"International conference on machine learning. PMLR, 3319--3328","author":"Sundararajan Mukund","year":"2017","unstructured":"Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017. Axiomatic attribution for deep networks. In International conference on machine learning. PMLR, 3319--3328."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/290941.290947"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277766"},{"key":"e_1_3_2_2_33_1","volume-title":"Attention is all you need. Advances in neural information processing systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_2_34_1","volume-title":"Gradient-based analysis of NLP models is manipulable. arXiv preprint arXiv:2010.05419","author":"Wang Junlin","year":"2020","unstructured":"Junlin Wang, Jens Tuyls, Eric Wallace, and Sameer Singh. 2020. Gradient-based analysis of NLP models is manipulable. arXiv preprint arXiv:2010.05419 (2020)."},{"key":"e_1_3_2_2_35_1","volume-title":"An In-depth Investigation of User Response Simulation for Conversational Search. arXiv preprint arXiv:2304.07944","author":"Wang Zhenduo","year":"2023","unstructured":"Zhenduo Wang, Zhichao Xu, Qingyao Ai, and Vivek Srikumar. 2023. An In-depth Investigation of User Response Simulation for Conversational Search. arXiv preprint arXiv:2304.07944 (2023)."},{"key":"e_1_3_2_2_36_1","volume-title":"Conditional self-attention for query-based summarization. arXiv preprint arXiv:2002.07338","author":"Xie Yujia","year":"2020","unstructured":"Yujia Xie, Tianyi Zhou, Yi Mao, and Weizhu Chen. 2020. Conditional self-attention for query-based summarization. arXiv preprint arXiv:2002.07338 (2020)."}],"event":{"name":"SIGIR '23: The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Taipei Taiwan","acronym":"SIGIR '23"},"container-title":["Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591936","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3539618.3591936","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:07Z","timestamp":1750178287000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3539618.3591936"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,18]]},"references-count":36,"alternative-id":["10.1145\/3539618.3591936","10.1145\/3539618"],"URL":"https:\/\/doi.org\/10.1145\/3539618.3591936","relation":{},"subject":[],"published":{"date-parts":[[2023,7,18]]},"assertion":[{"value":"2023-07-18","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}