Shaping Political Discourse using multi-source News Summarization
This addresses the challenge of political bias in news summarization for users seeking balanced information, though it appears incremental as it builds on existing multi-document summarization techniques.
The paper tackles the problem of generating unbiased summaries from multiple news documents by developing a machine learning model that samples input equally from all aspects of a topic, even when most sources are biased in one direction.
Multi-document summarization is the process of automatically generating a concise summary of multiple documents related to the same topic. This summary can help users quickly understand the key information from a large collection of documents. Multi-document summarization systems are more complex than single-document summarization systems due to the need to identify and combine information from multiple sources. In this paper, we have developed a machine learning model that generates a concise summary of a topic from multiple news documents. The model is designed to be unbiased by sampling its input equally from all the different aspects of the topic, even if the majority of the news sources lean one way.