SUMIE: A Synthetic Benchmark for Incremental Entity Summarization

• URL: http://arxiv.org/abs/2406.05079v1
• Date: Fri, 7 Jun 2024 16:49:21 GMT
• Title: SUMIE: A Synthetic Benchmark for Incremental Entity Summarization
• Authors: Eunjeong Hwang, Yichao Zhou, Beliz Gunel, James Bradley Wendt, Sandeep Tata,
• Abstract summary: No existing dataset adequately tests how well language models can incrementally update entity summaries. We introduce SUMIE, a fully synthetic dataset designed to expose real-world IES challenges. This dataset effectively highlights problems like incorrect entity association and incomplete information presentation.
• Score: 6.149024468471498
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: No existing dataset adequately tests how well language models can incrementally update entity summaries - a crucial ability as these models rapidly advance. The Incremental Entity Summarization (IES) task is vital for maintaining accurate, up-to-date knowledge. To address this, we introduce SUMIE, a fully synthetic dataset designed to expose real-world IES challenges. This dataset effectively highlights problems like incorrect entity association and incomplete information presentation. Unlike common synthetic datasets, ours captures the complexity and nuances found in real-world data. We generate informative and diverse attributes, summaries, and unstructured paragraphs in sequence, ensuring high quality. The alignment between generated summaries and paragraphs exceeds 96%, confirming the dataset's quality. Extensive experiments demonstrate the dataset's difficulty - state-of-the-art LLMs struggle to update summaries with an F1 higher than 80.4%. We will open source the benchmark and the evaluation metrics to help the community make progress on IES tasks.

Related papers

• Efficacy of Synthetic Data as a Benchmark [3.2968976262860408]
  We investigate the effectiveness of generating synthetic data through large language models (LLMs) Our experiments show that while synthetic data can effectively capture performance of various methods for simpler tasks, it falls short for more complex tasks like named entity recognition. We propose a new metric called the bias factor, which evaluates the biases introduced when the same LLM is used to both generate benchmarking data and to perform the tasks.
  arXiv  Detail & Related papers  (2024-09-18T13:20:23Z)
• 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)
• DiscoveryBench: Towards Data-Driven Discovery with Large Language Models [50.36636396660163]
  We present DiscoveryBench, the first comprehensive benchmark that formalizes the multi-step process of data-driven discovery. Our benchmark contains 264 tasks collected across 6 diverse domains, such as sociology and engineering. Our benchmark, thus, illustrates the challenges in autonomous data-driven discovery and serves as a valuable resource for the community to make progress.
  arXiv  Detail & Related papers  (2024-07-01T18:58:22Z)
• A Correlation- and Mean-Aware Loss Function and Benchmarking Framework to Improve GAN-based Tabular Data Synthesis [2.2451409468083114]
  We propose a novel correlation- and mean-aware loss function for generative adversarial networks (GANs) The proposed loss function demonstrates statistically significant improvements over existing methods in capturing the true data distribution. The benchmarking framework shows that the enhanced synthetic data quality leads to improved performance in downstream machine learning tasks.
  arXiv  Detail & Related papers  (2024-05-27T09:08:08Z)
• TarGEN: Targeted Data Generation with Large Language Models [51.87504111286201]
  TarGEN is a multi-step prompting strategy for generating high-quality synthetic datasets. We augment TarGEN with a method known as self-correction empowering LLMs to rectify inaccurately labeled instances. A comprehensive analysis of the synthetic dataset compared to the original dataset reveals similar or higher levels of dataset complexity and diversity.
  arXiv  Detail & Related papers  (2023-10-27T03:32:17Z)
• Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction [28.51694365908817]
  This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by large language models. We synthetically generate a dataset of 1.8M data points, establish its superior quality compared to existing datasets in a human evaluation.
  arXiv  Detail & Related papers  (2023-03-07T18:48:55Z)
• Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency [14.974996886744083]
  We release SummFC, a filtered summarization dataset with improved factual consistency. We argue that our dataset should become a valid benchmark for developing and evaluating summarization systems.
  arXiv  Detail & Related papers  (2022-10-31T15:04:20Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Unsupervised Opinion Summarization with Content Planning [58.5308638148329]
  We show that explicitly incorporating content planning in a summarization model yields output of higher quality. We also create synthetic datasets which are more natural, resembling real world document-summary pairs. Our approach outperforms competitive models in generating informative, coherent, and fluent summaries.
  arXiv  Detail & Related papers  (2020-12-14T18:41:58Z)
• Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation [101.26235068460551]
  Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. Models are typically fine-tuned on hundreds of thousands of data points, an infeasible requirement when applying summarization to new, niche domains. We introduce a novel and generalizable method, called WikiTransfer, for fine-tuning pretrained models for summarization in an unsupervised, dataset-specific manner.
  arXiv  Detail & Related papers  (2020-10-24T08:36:49Z)

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.