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Submodular functions have achieved state-of-the-art for almost all summarization problems. For example, work by Lin and Bilmes, 2012<ref>Hui Lin, Jeff Bilmes. "[https://arxiv.org/abs/1210.4871 Learning mixtures of submodular shells with application to document summarization]", UAI, 2012</ref> shows that submodular functions achieve the best results to date on DUC-04, DUC-05, DUC-06 and DUC-07 systems for document summarization. Similarly, work by Lin and Bilmes, 2011,<ref>Hui Lin, Jeff Bilmes. "[http://www.aclweb.org/anthology/P11-1052 A Class of Submodular Functions for Document Summarization]", The 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), 2011</ref> shows that many existing systems for automatic summarization are instances of submodular functions. This was a breakthrough result establishing submodular functions as the right models for summarization problems.{{citation needed|date=June 2018}}
Submodular Functions have also been used for other summarization tasks. Tschiatschek et al., 2014 show<ref>Sebastian Tschiatschek, Rishabh Iyer, Hoachen Wei and Jeff Bilmes, [http://papers.nips.cc/paper/5415-learning-mixtures-of-submodular-functions-for-image-collection-summarization.pdf Learning Mixtures of Submodular Functions for Image Collection Summarization], In Advances of Neural Information Processing Systems (NIPS), Montreal, Canada, December - 2014.</ref> that mixtures of submodular functions achieve state-of-the-art results for image collection summarization. Similarly, Bairi et al., 2015<ref>Ramakrishna Bairi, Rishabh Iyer, Ganesh Ramakrishnan and Jeff Bilmes, [http://www.aclweb.org/anthology/P15-1054 Summarizing Multi-Document Topic Hierarchies using Submodular Mixtures], To Appear In the Annual Meeting of the Association for Computational Linguistics (ACL), Beijing, China, July - 2015</ref> show the utility of submodular functions for summarizing multi-document topic hierarchies. Submodular Functions have also successfully been used for summarizing machine learning datasets.<ref>Kai Wei, Rishabh Iyer, and Jeff Bilmes, [http://www.jmlr.org/proceedings/papers/v37/wei15.pdf Submodularity in Data Subset Selection and Active Learning] {{Webarchive|url=https://web.archive.org/web/20170313220928/http://jmlr.org/proceedings/papers/v37/wei15.pdf |date=2017-03-13 }}, To Appear In Proc. International Conference on Machine Learning (ICML), Lille, France, June - 2015</ref>
===Applications===
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*{{cite book |last=Anne |first=Buist |year=2004 |title=Automatic Summarization of Meeting Data: A Feasibility Study|url=https://www.cs.ru.nl/~kraaijw/pubs/Biblio/papers/meeting_sum_tno.pdf}}
*{{cite book |last=Annie |first=Louis |year=2009 |title=Performance Confidence Estimation for Automatic Summarization|url=https://repository.upenn.edu/cgi/viewcontent.cgi?article=1762&context=cis_papers}}
*{{cite book |last=Elena |first=Lloret and Manuel, Palomar
*{{cite book |last=Andrew |first=Goldberg |year=2007 |title=Automatic Summarization}}
*{{cite book |last=Alrehamy |first=Hassan |year=2017 |title=Automatic Keyphrases Extraction |volume=650 |pages=222–235 |doi=10.1007/978-3-319-66939-7_19 |chapter=SemCluster: Unsupervised Automatic Keyphrase Extraction Using Affinity Propagation |series=Advances in Intelligent Systems and Computing |isbn=978-3-319-66938-0 }}
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