Examining the State-of-the-Art in News Timeline Summarization

• URL: http://arxiv.org/abs/2005.10107v1
• Date: Wed, 20 May 2020 15:06:39 GMT
• Title: Examining the State-of-the-Art in News Timeline Summarization
• Authors: Demian Gholipour Ghalandari and Georgiana Ifrim
• Abstract summary: Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the state-of-the-art on all tested benchmarks.
• Score: 10.16257074782054
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is mostly due to the focus on individual subtasks, such as date selection and date summarization, and to the previous lack of appropriate evaluation metrics for the full TLS task. In this paper, we compare different TLS strategies using appropriate evaluation frameworks, and propose a simple and effective combination of methods that improves over the state-of-the-art on all tested benchmarks. For a more robust evaluation, we also present a new TLS dataset, which is larger and spans longer time periods than previous datasets.

Related papers

• UniTTA: Unified Benchmark and Versatile Framework Towards Realistic Test-Time Adaptation [66.05528698010697]
  Test-Time Adaptation aims to adapt pre-trained models to the target domain during testing. Researchers have identified various challenging scenarios and developed diverse methods to address these challenges. We propose a Unified Test-Time Adaptation benchmark, which is comprehensive and widely applicable.
  arXiv  Detail & Related papers  (2024-07-29T15:04:53Z)
• Write Summary Step-by-Step: A Pilot Study of Stepwise Summarization [48.57273563299046]
  We propose the task of Stepwise Summarization, which aims to generate a new appended summary each time a new document is proposed. The appended summary should not only summarize the newly added content but also be coherent with the previous summary. We show that SSG achieves state-of-the-art performance in terms of both automatic metrics and human evaluations.
  arXiv  Detail & Related papers  (2024-06-08T05:37:26Z)
• Analyzing Temporal Complex Events with Large Language Models? A Benchmark towards Temporal, Long Context Understanding [57.62275091656578]
  We refer to the complex events composed of many news articles over an extended period as Temporal Complex Event (TCE) This paper proposes a novel approach using Large Language Models (LLMs) to systematically extract and analyze the event chain within TCE.
  arXiv  Detail & Related papers  (2024-06-04T16:42:17Z)
• Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction [36.915250638481986]
  We introduce LiveSum, a new benchmark dataset for generating summary tables of competitions based on real-time commentary texts. We evaluate the performances of state-of-the-art Large Language Models on this task in both fine-tuning and zero-shot settings. We additionally propose a novel pipeline called $T3$(Text-Tuple-Table) to improve their performances.
  arXiv  Detail & Related papers  (2024-04-22T14:31:28Z)
• Background Summarization of Event Timelines [13.264991569806572]
  We introduce the task of background news summarization, which complements each timeline update with a background summary of relevant preceding events. We construct a dataset by merging existing timeline datasets and asking human annotators to write a background summary for each timestep of each news event. We establish strong baseline performance using state-of-the-art summarization systems and propose a query-focused variant to generate background summaries.
  arXiv  Detail & Related papers  (2023-10-24T21:30:15Z)
• Prior-Free Continual Learning with Unlabeled Data in the Wild [24.14279172551939]
  We propose a Prior-Free Continual Learning (PFCL) method to incrementally update a trained model on new tasks. PFCL learns new tasks without knowing the task identity or any previous data. Our experiments show that our PFCL method significantly mitigates forgetting in all three learning scenarios.
  arXiv  Detail & Related papers  (2023-10-16T13:59:56Z)
• Evaluation of Faithfulness Using the Longest Supported Subsequence [52.27522262537075]
  We introduce a novel approach to evaluate faithfulness of machine-generated text by computing the longest noncontinuous of the claim that is supported by the context. Using a new human-annotated dataset, we finetune a model to generate Longest Supported Subsequence (LSS) Our proposed metric demonstrates an 18% enhancement over the prevailing state-of-the-art metric for faithfulness on our dataset.
  arXiv  Detail & Related papers  (2023-08-23T14:18:44Z)
• RDumb: A simple approach that questions our progress in continual test-time adaptation [12.374649969346441]
  Test-Time Adaptation (TTA) allows to update pre-trained models to changing data distributions at deployment time. Recent work proposed and applied methods for continual adaptation over long timescales. We find that eventually all but one state-of-the-art methods collapse and perform worse than a non-adapting model.
  arXiv  Detail & Related papers  (2023-06-08T17:52:34Z)
• Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [107.05966685291067]
  We propose test-time prompt tuning (TPT) to learn adaptive prompts on the fly with a single test sample. TPT improves the zero-shot top-1 accuracy of CLIP by 3.6% on average. In evaluating cross-dataset generalization with unseen categories, TPT performs on par with the state-of-the-art approaches that use additional training data.
  arXiv  Detail & Related papers  (2022-09-15T17:55:11Z)
• The Surprising Performance of Simple Baselines for Misinformation Detection [4.060731229044571]
  We examine the performance of a broad set of modern transformer-based language models. We present our framework as a baseline for creating and evaluating new methods for misinformation detection.
  arXiv  Detail & Related papers  (2021-04-14T16:25:22Z)
• Revisiting LSTM Networks for Semi-Supervised Text Classification via Mixed Objective Function [106.69643619725652]
  We develop a training strategy that allows even a simple BiLSTM model, when trained with cross-entropy loss, to achieve competitive results. We report state-of-the-art results for text classification task on several benchmark datasets.
  arXiv  Detail & Related papers  (2020-09-08T21:55:22Z)

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.