Editing Automatic summarization (section)

{{Short description|Computer-based method for summarizing a text}}
{{More citations needed|date=April 2022}}
'''Automatic summarization''' is the process of shortening a set of data computationally, to create a subset (a [[Abstract (summary)|summary]]) that represents the most important or relevant information within the original content. [[Artificial intelligence]] [[algorithm]]s are commonly developed and employed to achieve this, specialized for different types of data.

[[Plain text|Text]] summarization is usually implemented by [[natural language processing]] methods, designed to locate the most informative sentences in a given document.<ref name="Torres2014">{{cite book|author1=Torres-Moreno, Juan-Manuel|title=Automatic Text Summarization|url=https://www.wiley.com/en-gb/Automatic+Text+Summarization-p-9781848216686|date=1 October 2014|publisher=Wiley|isbn=978-1-848-21668-6|pages=320–}}</ref> On the other hand, visual content can be summarized using [[computer vision]] algorithms. [[Image]] summarization is the subject of ongoing research; existing approaches typically attempt to display the most representative images from a given image collection, or generate a video that only includes the most important content from the entire collection.<ref>{{Cite journal|last1=Pan|first1=Xingjia|last2=Tang|first2=Fan|last3=Dong|first3=Weiming|last4=Ma|first4=Chongyang|last5=Meng|first5=Yiping|last6=Huang|first6=Feiyue|last7=Lee|first7=Tong-Yee|last8=Xu|first8=Changsheng|date=2021-04-01|title=Content-Based Visual Summarization for Image Collection|journal=IEEE Transactions on Visualization and Computer Graphics|volume=27|issue=4|pages=2298–2312|doi=10.1109/tvcg.2019.2948611|pmid=31647438|s2cid=204865221|issn=1077-2626}}</ref><ref>{{Cite news|date=January 10, 2018|title=WIPO PUBLISHES PATENT OF KT FOR "IMAGE SUMMARIZATION SYSTEM AND METHOD" (SOUTH KOREAN INVENTORS)|work=US Fed News Service|url=https://www.proquest.com/docview/1986931333|access-date=January 22, 2021|id={{ProQuest|1986931333}}}}</ref><ref>{{Cite journal|last1=Li Tan|last2=Yangqiu Song|last3=Shixia Liu|author3-link=Shixia Liu|last4=Lexing Xie|date=February 2012|title=ImageHive: Interactive Content-Aware Image Summarization|journal=IEEE Computer Graphics and Applications|volume=32|issue=1|pages=46–55|doi=10.1109/mcg.2011.89|pmid=24808292|s2cid=7668289|issn=0272-1716}}</ref> Video summarization algorithms identify and extract from the original video content the most important frames (''key-frames''), and/or the most important video segments (''key-shots''), normally in a temporally ordered fashion.<ref name="PalPetrosino2012">{{cite book|author1=Sankar K. Pal|author2=Alfredo Petrosino|author3=Lucia Maddalena|title=Handbook on Soft Computing for Video Surveillance|url=https://books.google.com/books?id=O0fNBQAAQBAJ&q=video+surveillance+summarization&pg=PA81|date=25 January 2012|publisher=CRC Press|isbn=978-1-4398-5685-7|pages=81–}}</ref><ref name="Elhamifar2012">{{cite book |last1=Elhamifar |first1=Ehsan |last2=Sapiro |first2=Guillermo |last3=Vidal |first3=Rene |title=2012 IEEE Conference on Computer Vision and Pattern Recognition |chapter=See all by looking at a few: Sparse modeling for finding representative objects |url=https://ieeexplore.ieee.org/document/6247852  |year=2012 |pages=1600–1607 |publisher=IEEE |doi=10.1109/CVPR.2012.6247852 |isbn=978-1-4673-1228-8 |s2cid=5909301 |access-date=4 December 2022}}</ref><ref name="Mademlis2016">{{cite journal |last1=Mademlis |first1=Ioannis |last2=Tefas |first2=Anastasios |last3=Nikolaidis |first3=Nikos |last4=Pitas |first4=Ioannis |title=Multimodal stereoscopic movie summarization conforming to narrative characteristics |url=https://research-information.bris.ac.uk/files/111433536/Ioannis_Pitas_Multimodal_Stereoscopic_Movie_Summarization_Conforming_to_Narrative_Characteristics.pdf |journal=IEEE Transactions on Image Processing |year=2016 |volume=25 |issue=12 |pages=5828–5840 |publisher=IEEE |doi=10.1109/TIP.2016.2615289 |pmid=28113502 |bibcode=2016ITIP...25.5828M |hdl=1983/2bcdd7a5-825f-4ac9-90ec-f2f538bfcb72 |s2cid=18566122 |access-date=4 December 2022}}</ref><ref name="Mademlis2018">{{cite journal |last1=Mademlis |first1=Ioannis |last2=Tefas |first2=Anastasios |last3=Pitas |first3=Ioannis |title=A salient dictionary learning framework for activity video summarization via key-frame extraction |url=https://www.sciencedirect.com/science/article/abs/pii/S0020025517311398 |journal=Information Sciences |year=2018 |volume=432 |pages=319–331 |publisher=Elsevier |doi=10.1016/j.ins.2017.12.020 |access-date=4 December 2022}}</ref> Video summaries simply retain a carefully selected subset of the original video frames and, therefore, are not identical to the output of [[video synopsis]] algorithms, where ''new'' video frames are being synthesized based on the original video content.