Extractive Document Summarization

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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

2 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

12 shown of 12 papers with code (29 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.

• 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…
• Fine-tune BERT for Extractive Summarization
  25 Mar 2019 12 repositories listed Syntology ran 2 of 5 samples · 3 unverified
  BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks.
• AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization
  13 Apr 2020 3 repositories listed
  Redundancy-aware extractive summarization systems score the redundancy of the sentences to be included in a summary either jointly with their salience information or separately as an additional sentence scoring step.
• TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection
  20 Feb 2018 2 repositories listed
  Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events,…
• GoSum: Extractive Summarization of Long Documents by Reinforcement Learning and Graph Organized discourse state
  18 Nov 2022 1 repository listed
  In this paper, we propose GoSum, a novel graph and reinforcement learning based extractive model for long-paper summarization.
• GUSUM: Graph-based Unsupervised Summarization Using Sentence Features Scoring and Sentence-BERT
  1 Oct 2022 1 repository listed
  In this paper, we develop a Graph-Based Unsupervised Summarization(GUSUM) method for extractive text summarization based on the principle of including the most important sentences while excluding sentences with similar…
• DiMSum: Distributed and Multilingual Summarization of Financial Narratives
  1 Jun 2022 1 repository listed
  This paper was submitted for Financial Narrative Summarization (FNS) task in FNP-2022 workshop.
• Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers
  16 Oct 2020 1 repository listed Syntology ran 0 of 3 samples · 3 unverified · 3 pointer-only (licence)
  We also find in experiments that our model is less dependent on sentence positions.
• Heterogeneous Graph Neural Networks for Extractive Document Summarization
  26 Apr 2020 1 repository listed
  An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships.
• DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
  6 Nov 2018 1 repository listed
  We propose DeepChannel, a robust, data-efficient, and interpretable neural model for extractive document summarization.
• Neural Document Summarization by Jointly Learning to Score and Select Sentences
  6 Jul 2018 1 repository listed
  In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
• Document Modeling with External Attention for Sentence Extraction
  1 Jul 2018 1 repository listed
  Document modeling is essential to a variety of natural language understanding tasks.

Syntology lines on 3 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.