Editing Natural language generation (section)

===Automatic report generation===

From a commercial perspective, the most successful NLG applications
have been ''data-to-text'' systems which [[automatic summarization|generate textual summaries]] of databases and data sets; these
systems usually perform [[data analysis]] as well as text generation. Research has shown that textual summaries can be more effective than graphs and other visuals for decision support,<ref>{{cite journal |vauthors=Law A, Freer Y, Hunter J, Logie R, McIntosh N, Quinn J |title=A Comparison of Graphical and Textual Presentations of Time Series Data to Support Medical Decision Making in the Neonatal Intensive Care Unit |journal=Journal of Clinical Monitoring and Computing |volume=19 |pages=183–94 |year=2005 |doi=10.1007/s10877-005-0879-3 |pmid=16244840 |issue=3|s2cid=5569544 }}</ref><ref>{{cite journal |vauthors=Gkatzia D, Lemon O, Reiser V|title=Data-to-Text Generation Improves Decision-Making Under Uncertainty|journal=IEEE Computational Intelligence Magazine|volume=12|issue=3|pages=10–17|year=2017|doi=10.1109/MCI.2017.2708998|s2cid=9544295|url=https://napier-surface.worktribe.com/687579/1/gui-journal-paper-27Apr.pdf}}</ref><ref>{{Cite web | url=https://ehudreiter.com/2016/12/26/text-or-graphics/ | title=Text or Graphics?| date=2016-12-26}}</ref> and that computer-generated texts can be superior (from the reader's perspective) to human-written texts.<ref>{{cite journal |vauthors=Reiter E, Sripada S, Hunter J, Yu J, Davy I |title=Choosing Words in Computer-Generated Weather Forecasts |journal=Artificial Intelligence |volume=167 |issue= 1–2|pages=137–69 |year=2005 |doi=10.1016/j.artint.2005.06.006|doi-access=free }}</ref>

The first commercial data-to-text systems produced weather forecasts from weather data. The earliest such system to be deployed was FoG,<ref name=fog>{{cite journal |vauthors=Goldberg E, Driedger N, Kittredge R |title=Using Natural-Language Processing to Produce Weather Forecasts |journal=IEEE Expert |volume=9 |pages=45–53 |year=1994 |doi= 10.1109/64.294135 |issue= 2|s2cid=9709337 }}</ref> which was used by Environment Canada to generate weather forecasts in French and English in the early 1990s.  The success of FoG triggered other work, both research and commercial. Recent applications include the [[Met Office|UK Met Office's]] text-enhanced forecast.<ref>S Sripada, N Burnett, R Turner, J Mastin, D Evans(2014). [http://www.aclweb.org/anthology/W/W14/W14-4401.pdf Generating A Case Study: NLG meeting Weather Industry Demand for Quality and Quantity of Textual Weather Forecasts.] ''Proceedings of INLG 2014''</ref>

Data-to-text systems have since been applied in a range of settings. Following the minor earthquake near Beverly Hills, California on March 17, 2014, The Los Angeles Times reported details about the time, location and strength of the quake within 3 minutes of the event. This report was automatically generated by a 'robo-journalist', which converted the incoming data into text via a preset template.<ref>{{Cite web |last1=Schwencke |first1=Ken Schwencke Ken |last2=Journalist |first2=A. |last3=Programmer |first3=Computer |last4=in 2014 |first4=left the Los Angeles Times |date=2014-03-17 |title=Earthquake aftershock: 2.7 quake strikes near Westwood |url=https://www.latimes.com/local/lanow/earthquake-27-quake-strikes-near-westwood-california-rdivor-story.html |access-date=2022-06-03 |website=Los Angeles Times |language=en-US}}</ref><ref>{{Cite web |last=Levenson |first=Eric |date=2014-03-17 |title=L.A. Times Journalist Explains How a Bot Wrote His Earthquake Story for Him |url=https://www.theatlantic.com/technology/archive/2014/03/earthquake-bot-los-angeles-times/359261/ |access-date=2022-06-03 |website=The Atlantic |language=en}}</ref> Currently there is considerable commercial interest in using NLG to summarise financial and business data.  Indeed, [[Gartner]] has said that NLG will become a standard feature of 90% of modern BI and analytics platforms.<ref>{{Cite web | url=https://www.gartner.com/smarterwithgartner/nueral-networks-and-modern-bi-platforms-will-evolve-data-and-analytics/ | title=Neural Networks and Modern BI Platforms Will Evolve Data and Analytics}}</ref>  NLG is also being used commercially in [[automated journalism]], [[chatbot]]s, generating product descriptions for e-commerce sites, summarising medical records,<ref>{{cite conference |author=Harris MD |title=Building a Large-Scale Commercial NLG System for an EMR  |book-title=Proceedings of the Fifth International Natural Language Generation Conference  |pages=157–60  |year=2008 |url=http://www.aclweb.org/anthology/W08-1120.pdf }}</ref><ref name="portet">{{cite journal |vauthors=Portet F, Reiter E, Gatt A, Hunter J, Sripada S, Freer Y, Sykes C |title=Automatic Generation of Textual Summaries from Neonatal Intensive Care Data |journal=Artificial Intelligence |volume=173 |pages=789–816 |year=2009 |doi=10.1016/j.artint.2008.12.002 |issue=7–8|url=https://hal.archives-ouvertes.fr/hal-00953707/file/aij-bt45.pdf }}</ref> and enhancing [[accessibility]] (for example by describing graphs and data sets to blind people<ref>{{cite web |url=http://www.inf.udec.cl/~leo/iGraph.html |title=Welcome to the iGraph-Lite page |website=www.inf.udec.cl |url-status=dead |archive-url=https://web.archive.org/web/20100316130751/http://www.inf.udec.cl/~leo/igraph.html |archive-date=2010-03-16}}</ref>).

An example of an interactive use of NLG is the [[WYSIWYM (Meant)|WYSIWYM]] framework. It stands for ''What you see is what you meant'' and allows users to see and manipulate the continuously rendered view (NLG output) of an underlying formal language document (NLG input), thereby editing the formal language without learning it.

Looking ahead, the current progress in data-to-text generation paves the way for tailoring texts to specific audiences. For example, data from babies in neonatal care can be converted into text differently in a clinical setting, with different levels of technical detail and explanatory language, depending on intended recipient of the text (doctor, nurse, patient). The same idea can be applied in a sports setting, with different reports generated for fans of specific teams.<ref name=":0">{{cite arXiv |last1=Gatt |first1=Albert |last2=Krahmer |first2=Emiel |date=2018-01-29 |title=Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation |class=cs.CL |eprint=1703.09902 }}</ref>