Media framing bias can have a significant impact on political polarization. To address this issue and promote balanced news consumption, a new task of generating neutral summaries from multiple news headlines of varying political spectrums has been proposed. This paper presents a new dataset, insights about framing bias through a case study, and effective metrics and models for the task. Experimental analyses provide insights into remaining challenges and future directions. Multi-document summarization (MDS) is identified as a challenging subtask of automatic text summarization that aims to condense multiple documents into a concise yet informative summary. Deep neural models have been increasingly applied to the MDS task, utilizing large-scale datasets and pre-trained language models for improved performance. While various subtopics of MDS have been explored in previous works, there is limited research on generating framing bias-free summaries from multiple news articles. To fill this gap, the Neutral multi-news Summarization (NEUS) task is introduced in this work. Its objective is to generate neutral headline summaries from news headlines with varying degrees and orientations of political bias. The ALLSIDES dataset from Allsides.com provides triplets of news reports from left, right, and center American publishers along with expert-written neutral summaries. The dataset primarily focuses on U.S. political topics prone to media bias such as elections, the White House, and politics. By crawling headline triplets and neutral headline summaries from Allsides.com, the source inputs {HL, HR, HC} are used to generate the target output Hneu for the NEUS task. It's worth noting that even "center" media sources may exhibit framing bias due to editorial judgment biases. Therefore, automating roundup generation using MDS models capable of providing a neutral understanding of issues can help mitigate this gap. Overall, this research contributes to advancing the field by exploring how framing bias manifests in news headlines and providing insights into mitigating biases through automated summarization techniques. By addressing the challenge of generating unbiased summaries from politically diverse sources, this work paves the way for more balanced and informed news consumption practices in an era marked by increasing media polarization.
- Media framing bias can significantly impact political polarization
- A new task of generating neutral summaries from multiple news headlines has been proposed to address this issue and promote balanced news consumption
- The paper presents a new dataset, insights about framing bias through a case study, and effective metrics and models for the task
- Multi-document summarization (MDS) is identified as a challenging subtask that aims to condense multiple documents into concise yet informative summaries
- Deep neural models are increasingly applied to MDS tasks for improved performance
- The Neutral multi-news Summarization (NEUS) task is introduced to generate neutral headline summaries from news headlines with varying degrees of political bias
- The ALLSIDES dataset provides triplets of news reports from left, right, and center American publishers along with expert-written neutral summaries focusing on U.S. political topics prone to media bias
- Automating roundup generation using MDS models capable of providing a neutral understanding of issues can help mitigate framing biases in media sources
- This research contributes to advancing the field by exploring how framing bias manifests in news headlines and providing insights into mitigating biases through automated summarization techniques
Summary- Media framing bias can affect how people think about politics.
- A new way to create fair news summaries from different headlines has been suggested.
- The study introduces a new set of data, findings on framing bias, and tools for this task.
- Multi-document summarization is a hard job that tries to make short but informative summaries from many documents.
- Deep neural models are used more often to make these summaries better.
Definitions1. Media framing bias: When news is presented in a way that influences how people understand political issues.
2. Neutral summaries: Fair and unbiased descriptions of news events without taking sides.
3. Dataset: A collection of information or data used for research or analysis.
4. Metrics: Measurements used to evaluate the performance or quality of something.
5. Models: Tools or systems designed to perform specific tasks based on algorithms and data.
Media framing bias has been a growing concern in recent years, with the rise of social media and 24-hour news cycles. This phenomenon refers to the way in which news outlets can shape public perception by selectively presenting information and using specific language or visuals to influence how a story is perceived. This can have significant consequences, especially when it comes to political issues.
In response to this issue, a new research paper titled "Neutral Multi-News Summarization: A Dataset and Insights into Framing Bias" proposes a novel approach for promoting balanced news consumption. The paper introduces the Neutral multi-news Summarization (NEUS) task, which aims to generate neutral summaries from multiple news headlines of varying political spectrums. By addressing the challenge of generating unbiased summaries from politically diverse sources, this work paves the way for more balanced and informed news consumption practices in an era marked by increasing media polarization.
The NEUS task is part of the larger field of automatic text summarization, specifically focusing on multi-document summarization (MDS). MDS aims to condense multiple documents into a concise yet informative summary. While there has been previous research on various subtopics within MDS, such as extractive vs abstractive summarization or single vs multi-document summarization, there is limited research on generating framing bias-free summaries from multiple news articles.
To address this gap, the authors introduce the ALLSIDES dataset from Allsides.com. This dataset provides triplets of news reports from left-leaning, right-leaning, and center American publishers along with expert-written neutral summaries. The focus of the dataset is primarily on U.S. political topics that are prone to media bias such as elections, the White House, and politics.
Using these headline triplets and neutral headline summaries as source inputs {HL - left-leaning headline; HR - right-leaning headline; HC - center headline}, the objective is to generate a target output Hneu - a neutral headline summary. It's worth noting that even "center" media sources may exhibit framing bias due to editorial judgment biases. Therefore, automating roundup generation using MDS models capable of providing a neutral understanding of issues can help mitigate this gap.
The paper also provides insights into how framing bias manifests in news headlines through a case study on the ALLSIDES dataset. The results show that there is a clear difference in the language used by left-leaning and right-leaning headlines, with each side using specific keywords and phrases to frame the story in their favor. This highlights the need for unbiased summaries that present information from multiple perspectives.
To evaluate the performance of different models on the NEUS task, the authors propose effective metrics such as ROUGE (Recall-Oriented Understudy for Gisting Evaluation) and BERTScore (Bidirectional Encoder Representations from Transformers). These metrics take into account both content overlap and semantic similarity between generated summaries and expert-written neutral summaries.
Experimental analyses on various deep neural models trained on large-scale datasets and pre-trained language models show promising results for generating unbiased summaries. However, there are still challenges to be addressed, such as handling conflicting information or identifying subtle nuances in language that can affect neutrality.
In conclusion, this research contributes to advancing the field by exploring how framing bias manifests in news headlines and providing insights into mitigating biases through automated summarization techniques. By addressing the challenge of generating unbiased summaries from politically diverse sources, this work has significant implications for promoting balanced news consumption practices. In an era marked by increasing media polarization, it is crucial to have access to objective information from multiple perspectives. The NEUS task opens up new avenues for future research in this area and paves the way towards more informed decision-making based on accurate and unbiased news coverage.
