Context-Aware Hierarchical Merging for Long Document Summarization

• URL: http://arxiv.org/abs/2502.00977v1
• Date: Mon, 03 Feb 2025 01:14:31 GMT
• Title: Context-Aware Hierarchical Merging for Long Document Summarization
• Authors: Litu Ou, Mirella Lapata,
• Abstract summary: We propose different approaches to enrich hierarchical merging with context from the source document. Experimental results on datasets representing legal and narrative domains show that contextual augmentation consistently outperforms zero-shot and hierarchical merging baselines.
• Score: 56.96619074316232
• License:
• Abstract: Hierarchical Merging is a technique commonly used to summarize very long texts ($>$100K tokens) by breaking down the input into smaller sections, summarizing those sections individually, and then merging or combining those summaries into a final coherent summary. Although it helps address the limitations of large language models (LLMs) with fixed input length constraints, the recursive merging process can amplify LLM hallucinations, increasing the risk of factual inaccuracies. In this paper, we seek to mitigate hallucinations by enriching hierarchical merging with context from the source document. Specifically, we propose different approaches to contextual augmentation ranging from \emph{replacing} intermediate summaries with relevant input context, to \emph{refining} them while using the context as supporting evidence, and \emph{aligning} them implicitly (via citations) to the input. Experimental results on datasets representing legal and narrative domains show that contextual augmentation consistently outperforms zero-shot and hierarchical merging baselines for the Llama 3.1 model family. Our analysis further reveals that refinement methods tend to perform best when paired with extractive summarization for identifying relevant input.

Related papers

• Hierarchical Indexing for Retrieval-Augmented Opinion Summarization [60.5923941324953]
  We propose a method for unsupervised abstractive opinion summarization that combines the attributability and scalability of extractive approaches with the coherence and fluency of Large Language Models (LLMs) Our method, HIRO, learns an index structure that maps sentences to a path through a semantically organized discrete hierarchy. At inference time, we populate the index and use it to identify and retrieve clusters of sentences containing popular opinions from input reviews.
  arXiv  Detail & Related papers  (2024-03-01T10:38:07Z)
• On Context Utilization in Summarization with Large Language Models [83.84459732796302]
  Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens. We conduct the first comprehensive study on context utilization and position bias in summarization.
  arXiv  Detail & Related papers  (2023-10-16T16:45:12Z)
• Text Summarization with Oracle Expectation [88.39032981994535]
  Extractive summarization produces summaries by identifying and concatenating the most important sentences in a document. Most summarization datasets do not come with gold labels indicating whether document sentences are summary-worthy. We propose a simple yet effective labeling algorithm that creates soft, expectation-based sentence labels.
  arXiv  Detail & Related papers  (2022-09-26T14:10:08Z)
• A General Contextualized Rewriting Framework for Text Summarization [15.311467109946571]
  Exiting rewriting systems take each extractive sentence as the only input, which is relatively focused but can lose necessary background knowledge and discourse context. We formalize contextualized rewriting as a seq2seq with group-tag alignments, identifying extractive sentences through content-based addressing. Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning.
  arXiv  Detail & Related papers  (2022-07-13T03:55:57Z)
• Reinforcing Semantic-Symmetry for Document Summarization [15.113768658584979]
  Document summarization condenses a long document into a short version with salient information and accurate semantic descriptions. This paper introduces a new textbfreinforcing stextbfemantic-textbfsymmetry learning textbfmodel is proposed for document summarization. A series of experiments have been conducted on two wildly used benchmark datasets CNN/Daily Mail and BigPatent.
  arXiv  Detail & Related papers  (2021-12-14T17:41:37Z)
• Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and Documents [13.755637074366813]
  SummN is a simple, flexible, and effective multi-stage framework for input texts longer than the maximum context lengths of typical pretrained LMs. It can process input text of arbitrary length by adjusting the number of stages while keeping the LM context size fixed. Our experiments demonstrate that SummN significantly outperforms previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-10-16T06:19:54Z)
• Contextualized Rewriting for Text Summarization [10.666547385992935]
  We formalized rewriting as a seq2seq problem with group alignments. Results show that our approach significantly outperforms non-contextualized rewriting systems.
  arXiv  Detail & Related papers  (2021-01-31T05:35:57Z)
• Relation Clustering in Narrative Knowledge Graphs [71.98234178455398]
  relational sentences in the original text are embedded (with SBERT) and clustered in order to merge together semantically similar relations. Preliminary tests show that such clustering might successfully detect similar relations, and provide a valuable preprocessing for semi-supervised approaches.
  arXiv  Detail & Related papers  (2020-11-27T10:43:04Z)
• Extractive Summarization as Text Matching [123.09816729675838]
  This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. We formulate the extractive summarization task as a semantic text matching problem. We have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1)
  arXiv  Detail & Related papers  (2020-04-19T08:27:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.