维度融合：通过多源、多模态与多语言融合实现信息抽取与摘要生成
Converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion
AI总结：
针对大语言模型摘要方法依赖孤立数据源、信息覆盖有限、易生虚假内容且多语言多模态支持不足的问题，本文提出多源融合摘要方法，整合YouTube播放列表、预印本、Wikipedia等多源数据并转为统一文本表示，在最大化信息增益的同时减少重叠，生成高连贯摘要。
摘要：
大语言模型（LLMs）的最新进展催生了新的摘要生成策略，为抽取重要信息提供了丰富的工具集。然而，这类方法往往受限于对孤立数据源的依赖：可收集的信息量有限，覆盖的主题范围较窄，还可能存在生成虚假内容的问题，且对多语言、多模态数据的支持不足。本文提出一种新颖的摘要生成方法，通过利用多源数据的优势应对上述挑战，从而对复杂主题实现更全面、信息量更丰富的理解。本研究突破了文本文档这类传统单模态数据源的局限，整合了更多样化的数据类型，包括YouTube播放列表、预印本和Wikipedia页面。随后将这些异构数据源转换为统一的文本表示形式，以实现更全面的分析。这种多维度的摘要生成方法让我们能够从更广泛的数据源中抽取相关信息。该方法的核心原则是在最大化信息增益的同时，尽量减少信息重叠并保持高信息量，从而助力生成高度连贯的摘要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
