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Found 19 papers, 5 papers with code
Best practices for the human evaluation of automatically generated text
Currently, there is little agreement as to how Natural Language Generation (NLG) systems should be evaluated.
Enriching the WebNLG corpus
This paper describes the enrichment of WebNLG corpus (Gardent et al., 2017a, b), with the aim to further extend its usefulness as a resource for evaluating common NLG tasks, including Discourse Ordering, Lexicalization and Referring Expression Generation.
Automated learning of templates for data-to-text generation: comparing rule-based, statistical and neural methods
The current study investigated novel techniques and methods for trainable approaches to data-to-text generation.
Evaluating the text quality, human likeness and tailoring component of PASS: A Dutch data-to-text system for soccer
We present an evaluation of PASS, a data-to-text system that generates Dutch soccer reports from match statistics which are automatically tailored towards fans of one club or the other.
Aspect-based summarization of pros and cons in unstructured product reviews
In the second evaluation, the gold-standard pros and cons were assessed along with the system output.
Surface Realization Shared Task 2018 (SR18): The Tilburg University Approach
This study describes the approach developed by the Tilburg University team to the shallow task of the Multilingual Surface Realization Shared Task 2018 (SR18).
PASS: A Dutch data-to-text system for soccer, targeted towards specific audiences
We present PASS, a data-to-text system that generates Dutch soccer reports from match statistics.
Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation
In this paper, we study AMR-to-text generation, framing it as a translation task and comparing two different MT approaches (Phrase-based and Neural MT).
Generating flexible proper name references in text: Data, models and evaluation
The model relies on the REGnames corpus, a dataset with 53, 102 proper name references to 1, 000 people in different discourse contexts.
SatiricLR: a Language Resource of Satirical News Articles
We test the viability of our data on the task of classification of satire.
