Comparing Computational Architectures for Automated Journalism

• Yan V. Sym USP http://orcid.org/0000-0002-0401-0586
• João Gabriel M. Campos USP https://orcid.org/0000-0002-1106-3694
• Marcos M. José USP https://orcid.org/0000-0003-4663-4386
• Fabio G. Cozman USP https://orcid.org/0000-0003-4077-4935

DOI: https://doi.org/10.5753/eniac.2022.227426

Resumo

The majority of NLG systems have been designed following either a template-based or a pipeline-based architecture. Recent neural models for datato-text generation have been proposed with an end-to-end deep learning flavor, which handles non-linguistic input in natural language without explicit intermediary representations. This study compares the most often employed methods for generating Brazilian Portuguese texts from structured data. Results suggest that explicit intermediate steps in the generation process produce better texts than the ones generated by neural end-to-end architectures, avoiding data hallucination while better generalizing to unseen inputs. Code and corpus are publicly available.

Palavras-chave: Natural Language Generation, Automated Journalism, Blue Amazon

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