Editing Natural language generation (section)

==Stages==
The process to generate text can be as simple as keeping a list of canned text that is copied and pasted, possibly linked with some glue text. The results may be satisfactory in simple domains such as horoscope machines or generators of personalized business letters. However, a sophisticated NLG system needs to include stages of planning and merging of information to enable the generation of text that looks natural and does not become repetitive. The typical stages of natural-language generation, as proposed by Dale and Reiter,<ref name="Ehud">{{cite book |title=Building natural language generation systems |publisher=Cambridge University Press |location=Cambridge, U.K. |isbn=978-0-521-02451-8 |last1=Dale |first1=Robert |last2=Reiter |first2= Ehud |year=2000}}</ref> are:

'''[[Content determination]]''': Deciding what information to mention in the text.
For instance, in the pollen example above, deciding whether to explicitly mention that pollen
level is 7 in the southeast.

'''[[Document structuring]]''': Overall organisation of the information to convey.  For example, deciding to
describe the areas with high pollen levels first, instead of the areas with low pollen levels.

'''[[Aggregation (linguistics)|Aggregation]]''': Merging of similar sentences to improve readability and naturalness.
For instance, merging the two following sentences:
*''Grass pollen levels for Friday have increased from the moderate to high levels of yesterday'' and
*''Grass pollen levels will be around 6 to 7 across most parts of the country'' 
into the following single sentence:
*''Grass pollen levels for Friday have increased from the moderate to high levels of yesterday with values of around 6 to 7 across most parts of the country''.

'''[[Lexical choice]]''': Putting words to the concepts.  For example, deciding whether ''medium'' or ''moderate''
should be used when describing a pollen level of 4.

'''[[Referring expression generation]]''': Creating [[referring expression]]s that identify objects and regions.  For example, deciding to use
''in the Northern Isles and far northeast of mainland Scotland'' to refer to a certain region in Scotland.
This task also includes making decisions about [[pronouns]] and other types of
[[Anaphora (linguistics)|anaphora]].

'''[[realization (linguistics)|Realization]]''': Creating the actual text, which should be correct
according to the rules of
[[syntax]], [[morphology (linguistics)|morphology]], and [[orthography]].  For example, using ''will be'' for the future
tense of ''to be''.
<!-- Talk about the typical stages of an NLG system -->
<!-- Indicate some possible applications: report generation of stock exchange news or weather forecasts, for example -->

An alternative approach to NLG is to use "end-to-end" machine learning to build a system, without having separate stages as above.<ref>{{Cite web | url=http://www.macs.hw.ac.uk/InteractionLab/E2E/ | title=E2E NLG Challenge}}</ref>   In other words, we build an NLG system by training a machine learning algorithm (often an [[Long short-term memory|LSTM]]) on a large data set of input data and corresponding (human-written) output texts.  The end-to-end approach has perhaps been most successful in [[Automatic image annotation|image captioning]],<ref>{{Cite web | url=https://www.kaggle.com/c/datalabcup-image-caption | title=DataLabCup: Image Caption}}</ref> that is automatically generating a textual caption for an image.