  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.
