From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization

• URL: http://arxiv.org/abs/2404.03555v2
• Date: Fri, 12 Apr 2024 08:05:13 GMT
• Title: From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
• Authors: Botond Barta, Dorina Lakatos, Attila Nagy, Milán Konor Nyist, Judit Ács,
• Abstract summary: HunSum-2 is an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning.
• Score: 0.19107347888374507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces HunSum-2 an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning, preprocessing and deduplication. In addition to abstractive summarization we generate sentence-level labels for extractive summarization using sentence similarity. We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation. Our dataset, models and code are publicly available, encouraging replication, further research, and real-world applications across various domains.

Related papers

• 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)
• Abstractive Text Summarization Using the BRIO Training Paradigm [2.102846336724103]
  This paper presents a technique to improve abstractive summaries by fine-tuning pre-trained language models. We build a text summarization dataset for Vietnamese, called VieSum. We perform experiments with abstractive summarization models trained with the BRIO paradigm on the CNNDM and the VieSum datasets.
  arXiv  Detail & Related papers  (2023-05-23T05:09:53Z)
• mFACE: Multilingual Summarization with Factual Consistency Evaluation [79.60172087719356]
  Abstractive summarization has enjoyed renewed interest in recent years, thanks to pre-trained language models and the availability of large-scale datasets. Despite promising results, current models still suffer from generating factually inconsistent summaries. We leverage factual consistency evaluation models to improve multilingual summarization.
  arXiv  Detail & Related papers  (2022-12-20T19:52:41Z)
• GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state [6.4805900740861]
  We propose GoSum, a reinforcement-learning-based extractive model for long-paper summarization. GoSum encodes states by building a heterogeneous graph from different discourse levels for each input document. We evaluate the model on two datasets of scientific articles summarization: PubMed and arXiv.
  arXiv  Detail & Related papers  (2022-11-18T14:07:29Z)
• Salience Allocation as Guidance for Abstractive Summarization [61.31826412150143]
  We propose a novel summarization approach with a flexible and reliable salience guidance, namely SEASON (SaliencE Allocation as Guidance for Abstractive SummarizatiON) SEASON utilizes the allocation of salience expectation to guide abstractive summarization and adapts well to articles in different abstractiveness.
  arXiv  Detail & Related papers  (2022-10-22T02:13:44Z)
• Automated News Summarization Using Transformers [4.932130498861987]
  We will be presenting a comprehensive comparison of a few transformer architecture based pre-trained models for text summarization. For analysis and comparison, we have used the BBC news dataset that contains text data that can be used for summarization and human generated summaries.
  arXiv  Detail & Related papers  (2021-04-23T04:22:33Z)
• Bengali Abstractive News Summarization(BANS): A Neural Attention Approach [0.8793721044482612]
  We present a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences. We also prepared a dataset of more than 19k articles and corresponding human-written summaries collected from bangla.bdnews24.com1.
  arXiv  Detail & Related papers  (2020-12-03T08:17:31Z)
• Liputan6: A Large-scale Indonesian Dataset for Text Summarization [43.375797352517765]
  We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 document-summary pairs. We leverage pre-trained language models to develop benchmark extractive and abstractive summarization methods over the dataset.
  arXiv  Detail & Related papers  (2020-11-02T02:01:12Z)
• Few-Shot Learning for Opinion Summarization [117.70510762845338]
  Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text. Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
  arXiv  Detail & Related papers  (2020-04-30T15:37:38Z)
• 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)
• Pre-training for Abstractive Document Summarization by Reinstating Source Text [105.77348528847337]
  This paper presents three pre-training objectives which allow us to pre-train a Seq2Seq based abstractive summarization model on unlabeled text. Experiments on two benchmark summarization datasets show that all three objectives can improve performance upon baselines.
  arXiv  Detail & Related papers  (2020-04-04T05:06:26Z)

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.