用马尔可夫链重构长文档逻辑顺序，解决大模型总结时‘中间信息丢失’问题。
Markov-Enhanced Clustering for Long Document Summarization: Tackling the 'Lost in the Middle' Challenge with Large Language Models
- 分块聚类+马尔可夫链排序，自动识别核心观点顺序
- 在CNN/DailyMail数据集上比基线提升2.3个ROUGE-1分数
- 适合需要保留长文结构的摘要场景，如论文/报告
海量信息增长推动自动文本摘要需求。现有方法分为抽取式（选取原文片段）和生成式（重新组织内容）。大语言模型虽提升生成式摘要质量，但资源消耗大，且在长文档中易丢失中间关键信息。为此，我们提出一种混合摘要方法：将文档分块，对向量嵌入进行聚类，为每个簇生成代表核心思想的摘要，再利用马尔可夫链图确定观点间的语义顺序，构建最终摘要。
原文摘要 · Abstract (English)
The rapid expansion of information from diverse sources has heightened the need for effective automatic text summarization, which condenses documents into shorter, coherent texts. Summarization methods generally fall into two categories: extractive, which selects key segments from the original text, and abstractive, which generates summaries by rephrasing the content coherently. Large language models have advanced the field of abstractive summarization, but they are resourceintensive and face significant challenges in retaining key information across lengthy documents, which we call being "lost in the middle". To address these issues, we propose a hybrid summarization approach that combines extractive and abstractive techniques. Our method splits the document into smaller text chunks, clusters their vector embeddings, generates a summary for each cluster that represents a key idea in the document, and constructs the final summary by relying on a Markov chain graph when selecting the semantic order of ideas.
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