Controlled Neural Sentence-Level Reframing of News Articles (2021)

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 properly-framed text works well but with tradeoffs.

## introduction framing is a rhetorical means to emphasize a perspective of an issue @xcite . it is basically driven by argument selection @xcite and, hence, it belongs to the inventio canon in particular @xcite . the media employ framing to reorient how audiences think @xcite , or to promote a decided interpretation. for example, when talking about a certain law one may emphasize its economic impact or its consequences regarding crime. reframing means to change the perspective of an issue. it can be a strategy to communicate with opposing camps of audiences, and, sometimes, just (a) economic frame (original text) key congressional backers of the measure, sponsored by senator alan k. simpson, republican of arizona, and romano l. mazzoli, democrat of kentucky, wanted a flexible spending limit. implicit in the debate and the stalemate that left the bill to die when congress adjourned was a recognition that the cost of immigration reform would be high, although no one knew how high. without reform, though, the presence of what may be six million illegal aliens in this country exacts an economic and social toll. usually, reframing requires rewriting entire sentences rather than single words or phrases. to work in the real world, a computational reframing model needs to be able to rewrite sentences completely. at the same time, the model has to preserve the context, by maintaining coherence and topic consistency. towards these goals, we propose to treat reframing as a sentence-level fill-in-theblank task: given three consecutive sentences plus a target frame, mask the middle sentence and generate a sentence that connects the preceding and the succeeding sentence in a natural way and that conveys the target frame. this task implies three research questions: (1) how to tackle a sentencelevel fill-in-the-blank task in general? (2) how to generate a sentence with a specific frame? (3) how to make the sequence of sentences coherent? sentence-level blank filling is a new and unsolved task. we approach this task via controlled text generation, that is, by tweaking input and output of a sequence-to-sequence model where the masked sentence is the target output, and the preceding and the succeeding sentences are the inputs (section 3). for the second and third research question, we propose three training strategies: (a) framed-language pretraining, to finetune the model on all framed texts in order to learn the framed "language", (b) named-entity preservation, to support the model in maintaining important entities extracted from the masked sentence, and (c) adversarial learning, to show the model undesired output texts in order to learn to avoid them. based on the corpus of @xcite with annotated sentence-level frames (section 4), we empirically evaluate the pros and cons of each strategy and of combinations thereof (section 5). the results reveal that our approach changes sentences 1 ethical concerns regarding the correctness of reframed texts will be discussed in section 8. the contribution of this paper is threefold: (1) we demonstrate that sentence-level reframing can be tackled as a fill-in-the-blank task. (2) we propose three training strategies for controlled text generation problems such as reframing. (3) we provide empirical insights into unresolved aspects of the computational reframing of news articles. ## related work framing in media, particularly in news articles, has been investigated widely in the communication and journalism areas @xcite @xcite . it has been defined in different ways, ranging from a narrow view such as "make moral judgments" @xcite to a broader one including the "interpretative packages" @xcite . the set of frames for a certain topic can be issue-specific or generic. for example, the possible issue-specific frames for the topic of internet may include online communication and online services, whereas the generic ones include economically optimistic and political criticism @xcite . in this paper, we adopt the following narrow definition of frames: "a frame is an emphasis in the salience of different aspects of a topic" (de @xcite . in the area of natural language processing, media frame analysis is a relatively new topic. most existing works adopt the frame definition in social science, where framing refers to a choice of perspective @xcite . a more specific definition, which targets the argumentation contexts, defines a frame as a set of arguments that share an aspect @xcite . as for frame classification, most of the proposed approaches @xcite @xcite employ the media frames corpus @xcite , which is built upon the framing scheme of @xcite . following these approaches, we utilize the media frames corpus to build the dataset for our task. the study of media frames is closely related to the analysis of bias and unfairness conveyed by the media @xcite . for example, @xcite observed that the (potentially frame-specific) word choice may directly make a news article appear to have politically left or right bias. the only existing reframing approach that we are aware of is the one of @xcite . in that work, a new model for reframing is developed by identifying phrases indicative for specific frames, and then replacing phrases that belong to the source frame with some that belong to the target one. as such, most of the content of the reframed text is kept, and only a few words are replaced. in contrast, we deal with reframing at the sentence level, and we do not require parallel training pairs or a dictionary to correlate words and frames. in principle, reframing can be seen as a style transfer task @xcite @xcite . research on text style transfer focus on the areas of sentiment transfer (e.g., replacing 'gross' by 'awesome') @xcite and text simplification (e.g., replacing 'perched' by 'sat') @xcite . we applied recent style transfer models to our task @xcite , observing that these models perform very poorly (e.g., generating unreadable text). ## approach we now present our approach to sentence-level reframing. we discuss how we tackle the reframing problem as a fill-in-the-blank task, and we propose three training strategies to generate a sentence that is framed as desired and that fits to the surrounding text. figure 1 illustrates our approach. ## dataset in this section, we describe how we prepare the corpus we use in order to create training and test instances for the sentence-level fill-in-the-blank task. 2 we use the pretrained model en_core_web_lg from spacy for named entity recognition in our experiments. ## experiments this section reports on our experiments with our reframing approach (section 3) on the data from section 4. we present the results of the pilot study for the different reframing approaches, the metrics for automatic evaluation, and the design of crowdsourcing task for manual evaluation. ## results and discussion this section discusses the automatic and manual evaluation results, in order to then analyze how our three training strategies affect generation. finally, we show some examples from the reframed output and discuss the limitations of our approach. ## conclusion unlike several existing studies, where style transfer is addressed at the word or phrase level, this paper studies sentence-level style transfer for the problem of reframing news articles. we have cast this problem as a sentence-level fill-in-the-blank task, generating new sentences with target frames while maintaining their coherence and topic consistency with the surrounding context. to tackle the task, we have proposed three training strategies to control the framing and coherence of the generated sentences. evaluating these strategies automatically and manually, we found that, although it is not possible for any single strategy to fulfill the needs of reframing, combining the strategies leads to a successful reframing of news articles with reasonable coherence and topic consistency. even though we are aware of the limitation of our approach, we argue that such a sentence-level reframing is a big step towards full article reframing. in future work, we plan to cover a more finegrained set of frames, and to consider reframing at the level of paragraphs or entire articles.