Using Natural Language Generation to Bootstrap Missing Wikipedia Articles: A Human-centric Perspective

This revision has done a very good job at meeting the original review comments and is now a very strong paper. Perhaps its main contribution, apart from the very practical one of moving towards a more linguistically and culturally balanced wikipedia, is the very extensive and well explained evaluation stages. For the actual range of material generated automatically, this degree of extensive evaluation might seem a little like overkill, but its strenght is that this procedure can then naturally extend when the material generated itself becomes more substantial, and indeed for related generation tasks in this context. The paper is well written and it is now very clear what was happening at each stage and why each design decision was taken. I have just a few extremely minor corrections/typos and suggestions for final improvements, that I list below.

p. 1, l. 21: something odd here, "a 'introductory" --> "an introductory"

p. 2 left:
p. 2, l. 23: "produce one summary sentence" --> "produce a single summary sentence"
p. 2, l. 29: "as it is the case" --> "as is the case"
p. 2, l. 40: "to generate a short Wikipedia-style summary" --> "to generate a Wikipedia-style summary sentence"

p. 2 right:
p. 2, l. 26: "the text is fluent" --> "the sentence is fluent"
p. 2, l. 36: "the summaries generated" --> "the summary sentences generated"

p. 4:

the point here of using the generated versions as a way of keeping a more 'local'
expression that is not just a translation of the English is important and interesting. This
then adds a further potential consideration for the review of previous relevant text generation
work, particularly as given in §2.2. In addition to the work on producing text from
triples and similar, there is also the earlier work on multilingual natural language
generation, which was sometimes argued for as an alternative to translation precisely
because one would then be able to generate text fully within a target culture's norms
and not simply as a translation. Examples of such work and discussion include:

Kruijff, G.-J.; Teich, E.; Bateman, J. A.; Kruijff-Korbayová, I.; Skoumalová, H.; Sharoff, S.; Sokolova, L.; Hartley, T.; Staykova, K. & Hana, J. A multilingual system for text generation in three Slavic languages Proceedings of the 18th. International Conference on Computational Linguistics (COLING'2000), 2000, 474-480

Bateman, J. A.; Matthiessen, C. M. I. M. & Zeng, L. Multilingual natural language generation for multilingual software: a functional linguistic approach. Applied Artificial Intelligence, 1999, 13, 607-639

Hartley, A. & Paris, Cécile. Multilingual document production: from support for translating to support for authoring Machine Translation, 1997, 12, 109-129

and going all the way back to:

Kittredge, R.; Polguère, A. & Goldberg, E. Synthesizing weather reports from formatted data Proceedings of the 11th. International Conference on Computational Linguistics, International Committee on Computational Linguistics, 1986, 563-565

Some of the issues here may become more relevant still when generation moves beyond the single sentence stage.

p. 21, right, l. 39: "the generated summary" --> "the generated summary sentence"

The authors incorporated most of the comments into the revised version of the submission; the paper reads now much better and also reflects better the acquaintance of the authors with the state of the art in NLG. with one exception: the authors should still justify why they do not adapt as one of the baselines a state-of-the-art neural RDF-to-text model.

I refer to the review of the submission of the previous version of this paper. The major issues and many minor ones have been either considerably or entirely resolved. Based on that, I suggest to accept the paper for publication.