Text Summarization

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

37 leaderboard tables shown for this task, 37 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 37 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

98 datasets whose archive record lists this task, ordered by the archive's paper count. 30 shown of 98 until expanded.

Subtasks archive 2025-07-28

12 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 440 papers with code (1,340 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…
• Big Bird: Transformers for Longer Sequences
  28 Jul 2020 14 repositories listed Syntology ran 10 of 15 samples · 5 unverified · 11 pointer-only (licence)
  To remedy this, we propose, BigBird, a sparse attention mechanism that reduces this quadratic dependency to linear.
• Fastformer: Additive Attention Can Be All You Need
  20 Aug 2021 13 repositories listed Syntology ran 4 of 4 samples · 0 unverified · 3 pointer-only (licence)
  In this way, Fastformer can achieve effective context modeling with linear complexity.
• 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.
• WikiHow: A Large Scale Text Summarization Dataset
  18 Oct 2018 9 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Sequence-to-sequence models have recently gained the state of the art performance in summarization.
• Leveraging BERT for Extractive Text Summarization on Lectures
  7 Jun 2019 8 repositories listed
  This paper reports on the project called Lecture Summarization Service, a python based RESTful service that utilizes the BERT model for text embeddings and KMeans clustering to identify sentences closes to the centroid…
• NLG Evaluation Metrics Beyond Correlation Analysis: An Empirical Metric Preference Checklist
  15 May 2023 7 repositories listed
  Our proposed framework provides access: (i) for verifying whether automatic metrics are faithful to human preference, regardless of their correlation level to human; and (ii) for inspecting the strengths and limitations…
• Leveraging Pre-trained Checkpoints for Sequence Generation Tasks
  29 Jul 2019 7 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.
• MASS: Masked Sequence to Sequence Pre-training for Language Generation
  7 May 2019 7 repositories listed
  Pre-training and fine-tuning, e.
• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
  14 Nov 2016 7 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art.
• Incorporating Copying Mechanism in Sequence-to-Sequence Learning
  21 Mar 2016 7 repositories listed
  CopyNet can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence.
• SummEval: Re-evaluating Summarization Evaluation
  24 Jul 2020 6 repositories listed Syntology ran 0 of 6 samples · 6 unverified
  The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress.
• MTEB: Massive Text Embedding Benchmark
  13 Oct 2022 5 repositories listed Syntology ran 3 of 13 samples · 10 unverified
  MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages.
• PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
  26 Apr 2021 5 repositories listed
  To enhance the generalization ability of PanGu-α, we collect 1.
• BARThez: a Skilled Pretrained French Sequence-to-Sequence Model
  23 Oct 2020 5 repositories listed
  We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT.
• 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.
• 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.
• LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models
  21 Sep 2023 4 repositories listed Syntology ran 11 of 13 samples · 2 unverified
  For example, training on the context length of 8192 needs 16x computational costs in self-attention layers as that of 2048.
• OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
  7 Feb 2022 4 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  In this work, we pursue a unified paradigm for multimodal pretraining to break the scaffolds of complex task/modality-specific customization.
• 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.
• 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.
• A Neural Attention Model for Abstractive Sentence Summarization
  2 Sep 2015 4 repositories listed
  Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build.
• Text-Tuple-Table: Towards Information Integration in Text-to-Table Generation via Global Tuple Extraction
  22 Apr 2024 3 repositories listed Syntology ran 8 of 10 samples · 2 unverified · 9 pointer-only (licence)
  The task of condensing large chunks of textual information into concise and structured tables has gained attention recently due to the emergence of Large Language Models (LLMs) and their potential benefit for downstream…

Syntology lines on 19 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.