應用對話語篇剖析於兩階段會議摘要之研究

Leveraging Dialogue Discourse Parsing in a Two-Stage Framework for Meeting Summarization

會議摘要旨在從冗長的會議紀錄中，生成出簡潔並包含重要資訊的文本內容，能夠幫助參與者快速掌握會議的核心要點。然而，會議記錄通常具有複雜的對話結構，如不完整的句子和分散在各個話語中的資訊。此外，文本的長度經常超出了預訓練語言模型能夠處理的長度。本文提出了一種針對「長輸入文本」和「對話式結構」的兩階段摘要生成框架，首先進行文本擷取，從中篩選出重要的文本片段，然後基於這些片段進行摘要生成。對於複雜的對話結構，對話語篇剖析能夠理解話語間的關係，並將其畫成樹狀結構。我們選取較具結構的文本作為擷取階段的輸出，以增加資訊的密度且提供更結構化的對話文本作為生成器的輸入。實驗結果表明，我們的方法可以提升最終生成摘要的表現。

Meeting summarization aims to distill meaningful information from lengthy meeting transcripts into concise texts, allowing participants to grasp key points quickly. However, meeting transcripts often feature complex dialogue structures, such as incomplete sentences and information scattered across multiple utterances. Additionally, the length of these transcripts often exceeds the maximum input limit for pretrained language models. In this paper, we introduce a two-stage summarization framework specifically designed for long-input texts and complex dialogue structures. First, we extract key segments from the original transcript. Second, we generate the summary based on these extracted segments. To address the complexity of dialogue structures, we employ dialogue discourse parsing to comprehend the relationships between utterances, which we represent in a treelike structure. We select more structured text as the output from the extraction phase to enhance information density, thereby providing a more organized input for the summary generator. Experimental results demonstrate that our approach significantly improves the quality of the generated summaries.