Abstractive Text Summarization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

20 leaderboard tables shown for this task, 15 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 20 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

52 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 52 until expanded.

Subtasks archive 2025-07-28

3 subtasks in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 362 papers with code (846 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Attention Is All You Need
  12 Jun 2017 595 repositories listed Syntology ran 600 of 946 samples · 346 unverified · 451 pointer-only (licence)
  The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration.
• BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
  29 Oct 2019 47 repositories listed Syntology ran 22 of 53 samples · 31 unverified · 7 pointer-only (licence)
  We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single…
• Get To The Point: Summarization with Pointer-Generator Networks
  14 Apr 2017 39 repositories listed Syntology ran 30 of 64 samples · 34 unverified · 44 pointer-only (licence)
  Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
• PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
  18 Dec 2019 19 repositories listed Syntology ran 1 of 19 samples · 18 unverified
  Recent work pre-training Transformers with self-supervised objectives on large text corpora has shown great success when fine-tuned on downstream NLP tasks including text summarization.
• Text Summarization with Pretrained Encoders
  22 Aug 2019 19 repositories listed Syntology ran 7 of 21 samples · 14 unverified
  For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained…
• A Deep Reinforced Model for Abstractive Summarization
  11 May 2017 10 repositories listed
  We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and…
• Unified Language Model Pre-training for Natural Language Understanding and Generation
  8 May 2019 9 repositories listed
  This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks.
• R-Drop: Regularized Dropout for Neural Networks
  28 Jun 2021 8 repositories listed Syntology ran 2 of 6 samples · 4 unverified
  Dropout is a powerful and widely used technique to regularize the training of deep neural networks.
• GLM: General Language Model Pretraining with Autoregressive Blank Infilling
  18 Mar 2021 8 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  On a wide range of tasks across NLU, conditional and unconditional generation, GLM outperforms BERT, T5, and GPT given the same model sizes and data, and achieves the best performance from a single pretrained model with…
• ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation
  26 Jan 2020 5 repositories listed
  Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks.
• ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training
  13 Jan 2020 5 repositories listed
  This paper presents a new sequence-to-sequence pre-training model called ProphetNet, which introduces a novel self-supervised objective named future n-gram prediction and the proposed n-stream self-attention mechanism.
• Encode, Tag, Realize: High-Precision Text Editing
  3 Sep 2019 5 repositories listed Syntology ran 2 of 11 samples · 9 unverified
  We propose LaserTagger - a sequence tagging approach that casts text generation as a text editing task.
• Neural Abstractive Text Summarization with Sequence-to-Sequence Models
  5 Dec 2018 5 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  As part of this survey, we also develop an open source library, namely, Neural Abstractive Text Summarizer (NATS) toolkit, for the abstractive text summarization.
• Bottom-Up Abstractive Summarization
  31 Aug 2018 5 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  We use this selector as a bottom-up attention step to constrain the model to likely phrases.
• LongT5: Efficient Text-To-Text Transformer for Long Sequences
  15 Dec 2021 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Recent work has shown that either (1) increasing the input length or (2) increasing model size can improve the performance of Transformer-based neural models.
• TLDR: Extreme Summarization of Scientific Documents
  30 Apr 2020 4 repositories listed Syntology ran 12 of 19 samples · 7 unverified · 4 pointer-only (licence)
  We introduce TLDR generation, a new form of extreme summarization, for scientific papers.
• Evaluating the Factual Consistency of Abstractive Text Summarization
  28 Oct 2019 4 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  Currently used metrics for assessing summarization algorithms do not account for whether summaries are factually consistent with source documents.
• Pretraining-Based Natural Language Generation for Text Summarization
  25 Feb 2019 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  For the decoder, there are two stages in our model, in the first stage, we use a Transformer-based decoder to generate a draft output sequence.
• Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
  14 May 2018 4 repositories listed
  We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations.
• Global Encoding for Abstractive Summarization
  10 May 2018 4 repositories listed Syntology ran 1 of 7 samples · 6 unverified
  To tackle the problem, we propose a global encoding framework, which controls the information flow from the encoder to the decoder based on the global information of the source context.
• Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
  19 Feb 2016 4 repositories listed Syntology ran 5 of 6 samples · 1 unverified
  In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora.
• Mutual Information Alleviates Hallucinations in Abstractive Summarization
  24 Oct 2022 3 repositories listed Syntology ran 2 of 12 samples · 10 unverified · 1 pointer-only (licence)
  Despite significant progress in the quality of language generated from abstractive summarization models, these models still exhibit the tendency to hallucinate, i.
• FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations
  13 Apr 2022 3 repositories listed
  Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world…
• BRIO: Bringing Order to Abstractive Summarization
  31 Mar 2022 3 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  Abstractive summarization models are commonly trained using maximum likelihood estimation, which assumes a deterministic (one-point) target distribution in which an ideal model will assign all the probability mass to…
• Locally Typical Sampling
  1 Feb 2022 3 repositories listed Syntology ran 6 of 25 samples · 19 unverified · 15 pointer-only (licence)
  Automatic and human evaluations show that, in comparison to nucleus and top-k sampling, locally typical sampling offers competitive performance (in both abstractive summarization and story generation) in terms of…
• PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
  16 Oct 2021 3 repositories listed Syntology ran 4 of 7 samples · 3 unverified
  We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data.
• BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
  20 Sep 2021 3 repositories listed
  We present BARTpho with two versions, BARTpho-syllable and BARTpho-word, which are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese.
• CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization
  19 Sep 2021 3 repositories listed
  We study generating abstractive summaries that are faithful and factually consistent with the given articles.
• Annotating and Modeling Fine-grained Factuality in Summarization
  9 Apr 2021 3 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Recent pre-trained abstractive summarization systems have started to achieve credible performance, but a major barrier to their use in practice is their propensity to output summaries that are not faithful to the input…
• DebateSum: A large-scale argument mining and summarization dataset
  14 Nov 2020 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today.

Syntology lines on 22 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.