GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization

• URL: http://arxiv.org/abs/2311.07767v1
• Date: Mon, 13 Nov 2023 21:33:12 GMT
• Title: GreekT5: A Series of Greek Sequence-to-Sequence Models for News Summarization
• Authors: Nikolaos Giarelis, Charalampos Mastrokostas, Nikos Karacapilidis
• Abstract summary: This paper proposes a series of novel TS models for Greek news articles. The proposed models were thoroughly evaluated on the same dataset against GreekBART. Our evaluation results reveal that most of the proposed models significantly outperform GreekBART on various evaluation metrics.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Text summarization (TS) is a natural language processing (NLP) subtask pertaining to the automatic formulation of a concise and coherent summary that covers the major concepts and topics from one or multiple documents. Recent advancements in deep learning have led to the development of abstractive summarization transformer-based models, which outperform classical approaches. In any case, research in this field focuses on high resource languages such as English, while the corresponding work for low resource languages is still underdeveloped. Taking the above into account, this paper proposes a series of novel TS models for Greek news articles. The proposed models were thoroughly evaluated on the same dataset against GreekBART, which is the state-of-the-art model in Greek abstractive news summarization. Our evaluation results reveal that most of the proposed models significantly outperform GreekBART on various evaluation metrics. We make our evaluation code public, aiming to increase the reproducibility of this work and facilitate future research in the field.

Related papers

• Information-Theoretic Distillation for Reference-less Summarization [67.51150817011617]
  We present a novel framework to distill a powerful summarizer based on the information-theoretic objective for summarization. We start off from Pythia-2.8B as the teacher model, which is not yet capable of summarization. We arrive at a compact but powerful summarizer with only 568M parameters that performs competitively against ChatGPT.
  arXiv  Detail & Related papers  (2024-03-20T17:42:08Z)
• Exploring Precision and Recall to assess the quality and diversity of LLMs [82.21278402856079]
  We introduce a novel evaluation framework for Large Language Models (LLMs) such as textscLlama-2 and textscMistral. This approach allows for a nuanced assessment of the quality and diversity of generated text without the need for aligned corpora.
  arXiv  Detail & Related papers  (2024-02-16T13:53:26Z)
• Controllable Topic-Focused Abstractive Summarization [57.8015120583044]
  Controlled abstractive summarization focuses on producing condensed versions of a source article to cover specific aspects. This paper presents a new Transformer-based architecture capable of producing topic-focused summaries.
  arXiv  Detail & Related papers  (2023-11-12T03:51:38Z)
• GreekBART: The First Pretrained Greek Sequence-to-Sequence Model [13.429669368275318]
  We introduce GreekBART, the first Seq2Seq model based on BART-base architecture and pretrained on a large-scale Greek corpus. We evaluate and compare GreekBART against BART-random, Greek-BERT, and XLM-R on a variety of discriminative tasks.
  arXiv  Detail & Related papers  (2023-04-03T10:48:51Z)
• Large Language Models are Diverse Role-Players for Summarization Evaluation [82.31575622685902]
  A document summary's quality can be assessed by human annotators on various criteria, both objective ones like grammar and correctness, and subjective ones like informativeness, succinctness, and appeal. Most of the automatic evaluation methods like BLUE/ROUGE may be not able to adequately capture the above dimensions. We propose a new evaluation framework based on LLMs, which provides a comprehensive evaluation framework by comparing generated text and reference text from both objective and subjective aspects.
  arXiv  Detail & Related papers  (2023-03-27T10:40:59Z)
• 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)
• An Application of Pseudo-Log-Likelihoods to Natural Language Scoring [5.382454613390483]
  A language model with relatively few parameters and training steps can outperform it on a recent large data set. We produce some absolute state-of-the-art results for common sense reasoning in binary choice tasks. We argue that robustness of the smaller model ought to be understood in terms of compositionality.
  arXiv  Detail & Related papers  (2022-01-23T22:00:54Z)
• Multi-granular Legal Topic Classification on Greek Legislation [4.09134848993518]
  We study the task of classifying legal texts written in the Greek language. This is the first time the task of Greek legal text classification is considered in an open research project.
  arXiv  Detail & Related papers  (2021-09-30T17:43:00Z)
• BERT: A Review of Applications in Natural Language Processing and Understanding [0.0]
  We describe the application of one of the most popular deep learning-based language models - BERT. In preparing this review, the data of several dozen original scientific articles published over the past few years, which attracted the most attention in the scientific community, were systematized.
  arXiv  Detail & Related papers  (2021-03-22T15:34:39Z)
• 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)
• Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling [81.33107307509718]
  We propose a topic adaptive storyteller to model the ability of inter-topic generalization. We also propose a prototype encoding structure to model the ability of intra-topic derivation. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model.
  arXiv  Detail & Related papers  (2020-08-11T03:55:11Z)

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.