Controlled Neural Sentence-Level Reframing of News Articles

authored by

Wei Fan Chen, Khalid Al-Khati, Benno Stein, Henning Wachsmuth

Abstract

Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become necessary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences in news articles while maintaining their coherence to the context. We treat reframing as a sentence-level fill-in-the-blank task for which we train neural models on an existing media frame corpus. To guide the training, we propose three strategies: framed-language pretraining, named-entity preservation, and adversarial learning. We evaluate respective models automatically and manually for topic consistency, coherence, and successful reframing. Our results indicate that generating properlyframed text works well but with tradeoffs.

External Organisation(s)

Paderborn University
Bauhaus-Universität Weimar

Type

Conference contribution

Pages

2683-2693

No. of pages

11

Publication date

11.2021

Publication status

Published

ASJC Scopus subject areas

Language and Linguistics, Linguistics and Language

Electronic version(s)

https://doi.org/10.18653/v1/2021.findings-emnlp.228 (Access: Open)