  Recent advances in large language models (LLMs) have led to new summarization
strategies, offering an extensive toolkit for extracting important information.
However, these approaches are frequently limited by their reliance on isolated
sources of data. The amount of information that can be gathered is limited and
covers a smaller range of themes, which introduces the possibility of falsified
content and limited support for multilingual and multimodal data. The paper
proposes a novel approach to summarization that tackles such challenges by
utilizing the strength of multiple sources to deliver a more exhaustive and
informative understanding of intricate topics. The research progresses beyond
conventional, unimodal sources such as text documents and integrates a more
diverse range of data, including YouTube playlists, pre-prints, and Wikipedia
pages. The aforementioned varied sources are then converted into a unified
textual representation, enabling a more holistic analysis. This multifaceted
approach to summary generation empowers us to extract pertinent information
from a wider array of sources. The primary tenet of this approach is to
maximize information gain while minimizing information overlap and maintaining
a high level of informativeness, which encourages the generation of highly
coherent summaries.
