Editing Automatic summarization (section)

==History==
The first publication in the area dates back to 1957 <ref>Luhn, Hans Peter (1957). "A Statistical Approach to Mechanized Encoding and Searching of Literary Information" (PDF). IBM Journal of Research and Development. 1 (4): 309–317. doi:10.1147/rd.14.0309.</ref> ([[Hans Peter Luhn]]), starting with a statistical technique. Research increased significantly in 2015. [[Term frequency–inverse document frequency]] had been used by 2016. Pattern-based summarization was the most powerful option for multi-document summarization found by 2016. In the following year it was surpassed by [[latent semantic analysis]] (LSA) combined with [[non-negative matrix factorization]] (NMF). Although they did not replace other approaches and are often combined with them, by 2019 machine learning methods dominated the extractive summarization of single documents, which was considered to be nearing maturity. By 2020, the field was still very active and research is shifting towards abstractive summation and real-time summarization.<ref>{{Cite journal|date=2020-05-20|title=Review of automatic text summarization techniques & methods|journal=Journal of King Saud University - Computer and Information Sciences|language=en|doi=10.1016/j.jksuci.2020.05.006|issn=1319-1578|last1=Widyassari|first1=Adhika Pramita|last2=Rustad|first2=Supriadi|last3=Shidik|first3=Guruh Fajar|last4=Noersasongko|first4=Edi|last5=Syukur|first5=Abdul|last6=Affandy|first6=Affandy|last7=Setiadi|first7=De Rosal Ignatius Moses|volume=34 |issue=4 |pages=1029–1046 |doi-access=free}}</ref>

===Recent approaches===
Recently the rise of [[Transformer (machine learning model)|transformer models]] replacing more traditional [[Rnn (software)|RNN]] ([[LSTM]]) have provided a flexibility in the mapping of text sequences to text sequences of a different type, which is well suited to automatic summarization. This includes models such as T5<ref>{{Cite web |title=Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer |url=http://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html |access-date=2022-04-03 |website=Google AI Blog |date=24 February 2020 |language=en}}</ref> and Pegasus.<ref>Zhang, J., Zhao, Y., Saleh, M., & Liu, P. (2020, November). Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In International Conference on Machine Learning (pp. 11328-11339). PMLR.</ref>