Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization

• URL: http://arxiv.org/abs/2410.14545v1
• Date: Fri, 18 Oct 2024 15:40:48 GMT
• Title: Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization
• Authors: Frederic Kirstein, Terry Ruas, Robert Kratel, Bela Gipp,
• Abstract summary: Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification. Previous attempts to address these issues by considering related supplementary resources (e.g., presentation slides) alongside transcripts are hindered by models' limited context sizes. This work explores multi-source meeting summarization considering supplementary materials through a three-stage large language model approach.
• Score: 5.979778557940213
• License:
• Abstract: Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context understanding to fully comprehend the meetings' content. Previous attempts to address these issues by considering related supplementary resources (e.g., presentation slides) alongside transcripts are hindered by models' limited context sizes and handling the additional complexities of the multi-source tasks, such as identifying relevant information in additional files and seamlessly aligning it with the meeting content. This work explores multi-source meeting summarization considering supplementary materials through a three-stage large language model approach: identifying transcript passages needing additional context, inferring relevant details from supplementary materials and inserting them into the transcript, and generating a summary from this enriched transcript. Our multi-source approach enhances model understanding, increasing summary relevance by ~9% and producing more content-rich outputs. We introduce a personalization protocol that extracts participant characteristics and tailors summaries accordingly, improving informativeness by ~10%. This work further provides insights on performance-cost trade-offs across four leading model families, including edge-device capable options. Our approach can be extended to similar complex generative tasks benefitting from additional resources and personalization, such as dialogue systems and action planning.

Related papers

• Personalized Video Summarization using Text-Based Queries and Conditional Modeling [3.4447129363520337]
  This thesis explores enhancing video summarization by integrating text-based queries and conditional modeling. Evaluation metrics such as accuracy and F1-score assess the quality of the generated summaries.
  arXiv  Detail & Related papers  (2024-08-27T02:43:40Z)
• Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs [70.15262704746378]
  We propose a systematically created human-annotated dataset consisting of coherent summaries for five publicly available datasets and natural language user feedback. Preliminary experiments with Falcon-40B and Llama-2-13B show significant performance improvements (10% Rouge-L) in terms of producing coherent summaries.
  arXiv  Detail & Related papers  (2024-07-05T20:25:04Z)
• An End-to-End Speech Summarization Using Large Language Model [7.562198375754054]
  Speech Summarization (SSum) aims to generate human-like text summaries from spoken content. Research on large language models (LLMs) and multimodal information fusion has provided new insights. We propose an end-to-end SSum model that utilizes Q-Former as a connector for the audio-text modality.
  arXiv  Detail & Related papers  (2024-07-02T07:22:57Z)
• Converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion [0.0]
  The paper proposes a novel approach to summarization that tackles such challenges by utilizing the strength of multiple sources. 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.
  arXiv  Detail & Related papers  (2024-06-19T17:15:47Z)
• QFMTS: Generating Query-Focused Summaries over Multi-Table Inputs [63.98556480088152]
  Table summarization is a crucial task aimed at condensing information into concise and comprehensible textual summaries. We propose a novel method to address these limitations by introducing query-focused multi-table summarization. Our approach, which comprises a table serialization module, a summarization controller, and a large language model, generates query-dependent table summaries tailored to users' information needs.
  arXiv  Detail & Related papers  (2024-05-08T15:05:55Z)
• Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with Weak Supervision on Sentence Classification [91.13086984529706]
  Aspect-based meeting transcript summarization aims to produce multiple summaries. Traditional summarization methods produce one summary mixing information of all aspects. We propose a two-stage method for aspect-based meeting transcript summarization.
  arXiv  Detail & Related papers  (2023-11-07T19:06:31Z)
• Learning Summary-Worthy Visual Representation for Abstractive Summarization in Video [34.202514532882]
  We propose a novel approach to learning the summary-worthy visual representation that facilitates abstractive summarization. Our method exploits the summary-worthy information from both the cross-modal transcript data and the knowledge that distills from the pseudo summary.
  arXiv  Detail & Related papers  (2023-05-08T16:24:46Z)
• Summary-Oriented Vision Modeling for Multimodal Abstractive Summarization [63.320005222549646]
  Multimodal abstractive summarization (MAS) aims to produce a concise summary given the multimodal data (text and vision) We propose to improve the summary quality through summary-oriented visual features. Experiments on 44 languages, covering mid-high, low-, and zero-resource scenarios, verify the effectiveness and superiority of the proposed approach.
  arXiv  Detail & Related papers  (2022-12-15T09:05:26Z)
• Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization [13.117715760754077]
  We study the multi-perspective summarization of customer-care conversations between support agents and customers. We show that our approach supports models to generate multi-perspective summaries with a very small amount of annotated data.
  arXiv  Detail & Related papers  (2022-03-29T14:02:40Z)
• Unsupervised Summarization with Customized Granularities [76.26899748972423]
  We propose the first unsupervised multi-granularity summarization framework, GranuSum. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner.
  arXiv  Detail & Related papers  (2022-01-29T05:56:35Z)
• Dense Relational Image Captioning via Multi-task Triple-Stream Networks [95.0476489266988]
  We introduce dense captioning, a novel task which aims to generate captions with respect to information between objects in a visual scene. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding.
  arXiv  Detail & Related papers  (2020-10-08T09:17:55Z)

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.