LaMSUM: Creating Extractive Summaries of User Generated Content using LLMs

• URL: http://arxiv.org/abs/2406.15809v2
• Date: Thu, 22 Aug 2024 19:25:51 GMT
• Title: LaMSUM: Creating Extractive Summaries of User Generated Content using LLMs
• Authors: Garima Chhikara, Anurag Sharma, V. Gurucharan, Kripabandhu Ghosh, Abhijnan Chakraborty,
• Abstract summary: Large Language Models (LLMs) have demonstrated impressive performance across a wide range of NLP tasks, including summarization. We introduce LaMSUM, a novel framework designed to generate extractive summaries from large collections of user-generated text.
• Score: 6.770555526416268
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated impressive performance across a wide range of NLP tasks, including summarization. LLMs inherently produce abstractive summaries by paraphrasing the original text, while the generation of extractive summaries - selecting specific subsets from the original text - remains largely unexplored. LLMs have a limited context window size, restricting the amount of data that can be processed at once. We tackle this challenge by introducing LaMSUM, a novel multi-level framework designed to generate extractive summaries from large collections of user-generated text using LLMs. LaMSUM integrates summarization with different voting methods to achieve robust summaries. Extensive evaluation using four popular LLMs (Llama 3, Mixtral, Gemini, GPT-4o) demonstrates that LaMSUM outperforms state-of-the-art extractive summarization methods. Overall, this work represents one of the first attempts to achieve extractive summarization by leveraging the power of LLMs, and is likely to spark further interest within the research community.

Related papers

• Scaling Up Summarization: Leveraging Large Language Models for Long Text Extractive Summarization [0.27624021966289597]
  This paper introduces EYEGLAXS, a framework that leverages Large Language Models (LLMs) for extractive summarization. EYEGLAXS focuses on extractive summarization to ensure factual and grammatical integrity. The system sets new performance benchmarks on well-known datasets like PubMed and ArXiv.
  arXiv  Detail & Related papers  (2024-08-28T13:52:19Z)
• MixSumm: Topic-based Data Augmentation using LLMs for Low-resource Extractive Text Summarization [8.432813041805831]
  We propose MixSumm for low-resource extractive text summarization. Specifically, MixSumm prompts an open-source LLM, LLaMA-3-70b, to generate documents that mix information from multiple topics. We use ROUGE scores and L-Eval, a reference-free LLaMA-3-based evaluation method to measure the quality of generated summaries.
  arXiv  Detail & Related papers  (2024-07-10T03:25:47Z)
• 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)
• Assessing LLMs for Zero-shot Abstractive Summarization Through the Lens of Relevance Paraphrasing [37.400757839157116]
  Large Language Models (LLMs) have achieved state-of-the-art performance at zero-shot generation of abstractive summaries for given articles. We propose relevance paraphrasing, a simple strategy that can be used to measure the robustness of LLMs as summarizers.
  arXiv  Detail & Related papers  (2024-06-06T12:08:43Z)
• TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale [66.01943465390548]
  We introduce TriSum, a framework for distilling large language models' text summarization abilities into a compact, local model. Our method enhances local model performance on various benchmarks. It also improves interpretability by providing insights into the summarization rationale.
  arXiv  Detail & Related papers  (2024-03-15T14:36:38Z)
• BooookScore: A systematic exploration of book-length summarization in the era of LLMs [53.42917858142565]
  We develop an automatic metric, BooookScore, that measures the proportion of sentences in a summary that do not contain any of the identified error types. We find that closed-source LLMs such as GPT-4 and 2 produce summaries with higher BooookScore than those generated by open-source models.
  arXiv  Detail & Related papers  (2023-10-01T20:46:44Z)
• Summarization is (Almost) Dead [49.360752383801305]
  We develop new datasets and conduct human evaluation experiments to evaluate the zero-shot generation capability of large language models (LLMs) Our findings indicate a clear preference among human evaluators for LLM-generated summaries over human-written summaries and summaries generated by fine-tuned models.
  arXiv  Detail & Related papers  (2023-09-18T08:13:01Z)
• On Learning to Summarize with Large Language Models as References [101.79795027550959]
  Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets. We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
  arXiv  Detail & Related papers  (2023-05-23T16:56:04Z)
• Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method [35.181659789684545]
  Automatic summarization generates concise summaries that contain key ideas of source documents. References from CNN/DailyMail and BBC XSum are noisy, mainly in terms of factual hallucination and information redundancy. We propose a Summary Chain-of-Thought (SumCoT) technique to elicit LLMs to generate summaries step by step. Experimental results show our method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 in ROUGE-L.
  arXiv  Detail & Related papers  (2023-05-22T18:54:35Z)
• Zero-Shot Cross-Lingual Summarization via Large Language Models [108.30673793281987]
  Cross-lingual summarization ( CLS) generates a summary in a different target language. Recent emergence of Large Language Models (LLMs) has attracted wide attention from the computational linguistics community. In this report, we empirically use various prompts to guide LLMs to perform zero-shot CLS from different paradigms.
  arXiv  Detail & Related papers  (2023-02-28T01:27:37Z)

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.