SlowGuess/NAACL_2022_OCR huggingface dataset:
Since one common form of framing bias is the reporting of extra information (§4), summarization models, which extract commonly shared salient information, may already generate a neutral summaries to some extent. To test this, we conduct experiments using the following baselines.
- LEXRANK (Erkan and Radev, 2004): an extractive single-document summarization (SDS) model that extracts representative sentences that hold information common in both left- and right-leaning articles.
- BARTCNN: an abstractive SDS model that fine-tunes BART-large (Lewis et al., 2019), with 406M parameters, using the CNN/DailyMail (Hermann et al., 2015) dataset.
- BARTMULTI: a multi-document summarization (MDS) model that fine-tunes BART-large using Multi-News (Fabbri et al., 2019) dataset.
- PEGASUSMULTI: an MDS model that finetunes Pegasus-base (Zhang et al., 2019a), with 568M parameters, using the Multi-News dataset.
Since the summarization models are not trained with in-domain data, we provide another baseline model trained with in-domain data for a full picture.
- NEUSFT: a baseline that fine-tunes the BART-large model using ALLSIDES.
6.2 Our NEUS Models (NEUS-TITLE)
We design our models based on the second insight from the case study (§4) - the news title serves as an indicator of the framing bias in the corresponding article. We hypothesize that it would be helpful to divide-and-conquer by neutralizing the title first, then leveraging the "neutralized title" to guide the final neutral summary of the longer articles.
Multi-task learning (MTL) is a natural modeling choice because two sub-tasks are involved - title-level and article-level neutral summarization.
$^9$ Experimental details are provided in the appendix for reproducibility.
Models	Avg. Framing Bias Metric	Salient Info	Hallucination
\( Arousal_{+}\downarrow \)	\( Arousal_{-}\downarrow \)	\( Arousal_{sum}\downarrow \)	BLEU↑	ROUGE1-R↑	FeQA↑
All Source input	6.76	3.64	10.40	8.27	56.57%	-
LEXRANK	3.02	1.74	4.76	12.21	39.08%	53.44%
BARTCNN	2.09	1.23	3.32	10.49	35.63%	58.03%
PEGASUSMULTI	5.12	2.39	7.51	6.12	44.42%	22.24%
BARTMULTI	5.94	2.66	8.61	4.24	35.76%	21.06%
NEUSFT	1.86	1.00	2.85	11.67	35.11%	58.50%
NEUS-TITLE	1.69	0.83	2.53	12.05	36.07%	45.95%
Table 2: Experimental results for ALLSIDES test set. We provide the level of framing bias inherent in "source input" from the ALLSIDES test set to serve as a reference point for the framing bias metric. For framing bias metric, the lower number is the better  $(\downarrow)$ . For other scores, the higher number is the better  $(\uparrow)$ .
Meanwhile, we also have to ensure a hierarchical relationship between the two tasks in our MTL training because article-level neutral summarization leverages the generated neutral title as an additional resource. We use a simple technique to do hierarchical MTL by formatting our hierarchical data pair (title, article) in a single natural language text with identifier-tokens ("Title=>", "Article=>"). This technique allows us to optimize for both title and article neutral summarization tasks easily by optimizing for the negative log-likelihood of the single target Y. The auto-regressive nature of the decoder also ensures the hierarchical relationship between the title and article.
We train BART's autoregressive decoder to generate the target text  $Y$  formatted as follows:
$$
\mathrm {T I T L E} \Rightarrow T _ {n e u}. \mathrm {A R T I C L E} \Rightarrow A _ {n e u},
$$
where  $T_{neu}$  and  $A_{neu}$  denote the neutral title and neutral article summary.
The input  $X$  to our BART encoder is formatted similarly to the target text  $Y$ :
$$
\begin{array}{l} \mathrm {T I T L E} \Rightarrow T _ {L}. \mathrm {A R T I C L E} \Rightarrow A _ {L}. [ S E P ] \\ \mathrm {T I T L E} \Rightarrow T _ {C}. \text {A R T I C L E} \Rightarrow A _ {C}. [ S E P ] \\ \mathrm {T I T L E} \Rightarrow T _ {R}. \mathrm {A R T I C L E} \Rightarrow A _ {R}, \\ \end{array}
$$
where  $T_{L / C / R}$  and  $A_{L / C / R}$  denote the title and article from left-wing, center, and right-wing media, and [SEP] denotes the special token that separates different inputs. Note that the order of left, right, and center are randomly shuffled for each sample to discourage the model from learning spurious patterns from the input.
In this section, we point out noteworthy observations from the quantitative results in Table 2 along
with insights obtained through qualitative analysis. Table 3 shows generation examples that are most representative of the insights we share. $^{10}$
7.1 Main Results
Firstly, summarization models can reduce the framing bias to a certain degree (drop in  $Arousal_{sum}$  score from 10.40 to 4.76 and 3.32 for LEXRANK and BARTCNN). This is because informational framing bias is addressed when summarization models extract the most salient sentences, which contain common information from the inputs. However, summarization models, especially LEXRANK cannot handle the lexical framing bias, as shown in Table 3. Moreover, if we further observe the results of LEXRANK, it is one of the best performing models in terms of ROUGE1-R (39.08%), the standard metric for summarization performance, but not in terms of the framing bias metric. This suggests that having good summarization performance (ROUGE1-R) does not guarantee that the model is also neutral - i.e., the requirement for summaries to be neutral adds an extra dimension to the summarization task.
Secondly, one interesting pattern that deserves attention is that only the single-document summarization models (BARTCNN and LEXRANK) reduced framing bias well, not the multi-document summarization models (PEGASUSMULTI and BART-MULTI). This is rather surprising because our task setup is more similar to MDS than SDS. One of the major contributors to high bias in the MDS models is probably the hallucination because MDS models portray drastically poor hallucination performance than all the other models (both the MDS models PEGASUSMULTI and BARTMULTI achieve
10More examples are provided in the appendix.
SOURCE: <Left> Title: Here Are The 81 People And Entities Close To Trump Democrats Are Investigating. Article: Democrats on the House Judiciary Committee on Monday sent document requests to 81 agencies, entities and individuals close to President Donald Trump as part of a broad investigation into possible obstruction of justice, public corruption and other abuses of power. The list includes Trump's sons, Eric Trump and Donald Trump Jr., as well as his son-in-law, Jared Kushner.
<Center> Title: House Panel Requests Documents From Associates of Trump. Article: House Democrats intensified their investigations into President Trump and his associates Monday, demanding records from more than 80 people and organizations related to his business dealings, interactions with the Justice Department and communications with Russian President Vladimir Putin.
<Right> Title: Dems Continue Their Assault on The Trump Administration By Launching Another Probe. Article: Democrats are desperate to take down President Donald Trump. The Russia probe has proven to be ineffective and, quite frankly, a waste of time and taxpayer money. They didn't find what they wanted so now they're launching another probe.
TARGET: House Democrats launched a broad probe into President Trump on Monday, requesting documents from 81 agencies and individuals as they investigate his business dealings, interactions with Russia, and possible obstruction of justice.
**Lexrank:** Democrats are desperate to take down President Donald Trump. The Russia probe has proven to be ineffective and, quite frankly, a waste of time and taxpayer money.
NEUSFT: The Russia probe has proven to be ineffective and, quite frankly, a waste of time and taxpayer money.
NEUS-TITLE: TITLE  $\Rightarrow$  House Panel Requests Documents. ARTICLE  $\Longrightarrow$  The House Select Committee on Intelligence has requested documents from 81 people and entities close to President Trump, including his sons Eric and Donald Trump Jr., as well as Jared Kushner.
Table 3: Generation examples for analysis purposes. Red highlights the tokens identified by VAD lexicons. Refer to the appendix for more examples.
SOURCE: ... President Trump on Saturday blasted what he called the "phony" BuzzFeed story and the mainstream media's coverage of it...
MDS Hallucination: president trump on sunday slammed what he called called a “phony” story by the "dishonest" and "fake news" news outlet in a series of tweets. ... "the fake news media is working overtime to make this story look like it is true," trump tweeted. "they are trying to make it look like the president is trying to hide something, but it is not true!"
Table 4: Illustration of hallucinatory framing bias from MDS models and the corresponding "most relevant source snippet" from the source input. Refer to the appendix for more examples with full context.
22.24% and 21.06%, when most of the other models achieve over 50%).<sup>11</sup> This suggests that the framing bias of MDS models may be related to the hallucination of politically biased content. We investigate into this in the next subsection (§7.2).
Thirdly, although summarization models help reduce the framing bias scores, we, unsurprisingly, observe a more considerable bias reduction when training with in-domain data. NEUSFT shows a further drop across all framing bias metrics without sacrificing the ability to keep salient information. However, we observe that NEUSFT often copies directly without any neutral re-writing – e.g., the NEUSFT example shown in Table 3 is a direct copy of the sentence from the input source.
Lastly, we can achieve slightly further improvement with NEUS-TITLE across all metrics ex
cept the FeQA score. This model demonstrates a stronger tendency to paraphrase rather than directly copy, and has comparatively more neutral framing of the issue. As shown in Table 3, when LEXRANK and NEUSFT are focused on the "ineffectiveness of Russia probe", the TARGET and NEUS-TITLE focus on the start of the investigation with the request for documents. NEUS-TITLE also generate a title with a similar neutral frame to the TARGET, suggesting this title generation guided the correctly framed generation.
7.2 Further Analysis and Discussion
Q: Is hallucination contributing to the high framing bias in MDS models? Through qualitative analysis, we discovered the MDS generations were hallucinating politically controversial or sensational content that did not exist in the input sources. This is probably originating from the
<sup>11</sup>Note that  $22.24\%$  and  $21.06\%$  are already high FeQA scores, however, a comparatively low score in reference.
memorization of either the training data or the LM-pretraining corpus. For instance, in Table 4, we can observe stylistic bias being injected – “the ‘dishonest’ and ‘fake news’ news outlet”. Also, the excessive elaboration of the president’s comment towards the news media, which does not appear the in source or target, can be considered informational bias – “they are trying to make it look like the president is trying to hide something, but it is not true!” This analysis unveils the overlooked danger of hallucination, which is the risk of introducing political framing bias in summary generations. Note that this problem is not confined to MDS models only because other baseline models also have room for improvement in terms of the FeQA hallucination score.
Q: What are the remaining challenges and future directions? The experimental results of NEUS-TITLE suggest that there is room for improvement. We qualitatively checked some error cases and discovered that the title-generation is, unsurprisingly, not always accurate, and the error propagating from the title-generation step adversely affected the overall performance. Thus, one possible future direction would be to improve the neutral title generation, which would then improve the neutral summarization.
Another challenge is the subtle lexical bias involving nuanced word choices that manoeuvre readers to understand the events from biased frames. For example, "put on hold" and "stalled" both mean the same outcome, but the latter has a more negative connotations. Improving the model's awareness of such nuanced words or devising ways to incorporate style-transfer-based bias mitigation approaches (Liu et al., 2021) could be another helpful future direction.
We started the neutral summarization task assuming that framing bias originates from the source inputs. However, our results and analysis suggest that hallucination is another contributor to framing bias. Leveraging hallucination mitigation techniques would be a valuable future direction for the NEUS task. We believe it will help to reduce informational framing bias, although it may be less effective to lexical framing biases. Moreover, our work can also be used to facilitate hallucination research as well. We believe the proposed framing bias metric will help researchers evaluate hallucinatory phenomena from different angles other than "factuality". The proposed framing bias met
ric could also be adapted to the hallucination problem without a "neutral" reference. The source input can substitute the "neutral" reference to measure if the generated summary is more politically biased than the source - a potential indication of political hallucination.
We introduce a new task of Neutral Multi-News Summarization (NEUS) to mitigate media framing bias by providing a neutral summary of articles, along with the dataset ALLSIDES and a set of metrics. Throughout the work, we share insights to understand the challenges and future directions in the task. We show the relationships among polarity, extra information, and framing bias, which guides us to the metric design, while the insight that the title serves as an indicator of framing bias leads us to the model design. Our qualitative analysis reveals that hallucinatory content generated by models may also contribute to framing bias. We hope our work stimulates researchers to actively tackle political framing bias in both human-written and machine-generated texts.
The idea of unbiased journalism has always been challenged<sup>12</sup> because journalists will make their own editorial judgements that can never be guaranteed to be completely bias-free. Therefore, we propose to generate a comprehensive summary of articles from different political leanings, instead of trying to generate a gold standard "neutral" article.
One of the considerations is the bias induced by the computational approach. Automatic approaches replace a known source bias with another bias caused by human-annotated data or the machine learning models. Understanding the risk of uncontrolled adoption of such automatic tools, careful guidance should be provided in how to adopt them. For instance, an automatically generated neutral summary should be provided with reference to the original source instead of standing alone.
We use news from English-language sources only and largely American news outlets throughout this paper. Partisanship from this data refers to domestic American politics. We note that this work does not cover media bias at the international-level or in other languages. In future work, we
$^{12}$ https://www.allsides.com/blog/does-unbiased-news-actually-exist
will explore the application of our methodology to different cultures or languages. However, we hope the paradigm of NEUS, providing multiple sides to neutralize the view of an issue, can encourage future research in mitigating framing bias in other languages or cultures.
2021. Center – what does a "center" media bias rating mean?
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