Automatic summarization: Difference between revisions

Revision as of 19:05, 29 December 2023

Latest revision as of 21:53, 16 July 2025

(9 intermediate revisions by 7 users not shown)

Line 3:

'''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://ieeexploreresearch-information.ieeebris.orgac.uk/documentfiles/7583677111433536/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|url-access=subscription }}</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.

== Commercial products ==

Line 18:

===Abstractive-based summarization===

Abstractive summarization methods generate new text that did not exist in the original text.<ref>{{Cite book |last=Zhai |first=ChengXiang |url=https://www.worldcat.org/oclc/957355971 |title=Text data management and analysis : a practical introduction to information retrieval and text mining |date=2016 |others=Sean Massung |isbn=978-1-970001-19-8 |page=321 |___location=[New York, NY] |oclc=957355971}}</ref> This has been applied mainly for text. Abstractive methods build an internal semantic representation of the original content (often called a language model), and then use this representation to create a summary that is closer to what a human might express. Abstraction may transform the extracted content by [[automated paraphrasing|paraphrasing]] sections of the source document, to condense a text more strongly than extraction. Such transformation, however, is computationally much more challenging than extraction, involving both [[natural language processing]] and often a deep understanding of the ___domain of the original text in cases where the original document relates to a special field of knowledge. "Paraphrasing" is even more difficult to apply to images and videos, which is why most summarization systems are extractive.

===Aided summarization===

Line 57:

Designing a supervised keyphrase extraction system involves deciding on several choices (some of these apply to unsupervised, too). The first choice is exactly how to generate examples. Turney and others have used all possible unigrams, bigrams, and trigrams without intervening punctuation and after removing stopwords. Hulth showed that you can get some improvement by selecting examples to be sequences of tokens that match certain patterns of part-of-speech tags. Ideally, the mechanism for generating examples produces all the known labeled keyphrases as candidates, though this is often not the case. For example, if we use only unigrams, bigrams, and trigrams, then we will never be able to extract a known keyphrase containing four words. Thus, recall may suffer. However, generating too many examples can also lead to low precision.

We also need to create features that describe the examples and are informative enough to allow a learning algorithm to discriminate keyphrases from non- keyphrases. Typically features involve various term frequencies (how many times a phrase appears in the current text or in a larger corpus), the length of the example, relative position of the first occurrence, various booleanBoolean syntactic features (e.g., contains all caps), etc. The Turney paper used about 12 such features. Hulth uses a reduced set of features, which were found most successful in the KEA (Keyphrase Extraction Algorithm) work derived from Turney's seminal paper.

In the end, the system will need to return a list of keyphrases for a test document, so we need to have a way to limit the number. Ensemble methods (i.e., using votes from several classifiers) have been used to produce numeric scores that can be thresholded to provide a user-provided number of keyphrases. This is the technique used by Turney with C4.5 decision trees. Hulth used a single binary classifier so the learning algorithm implicitly determines the appropriate number.