---
title: "Bottom-Up Abstractive Summarization — Gehrmann 2018"
description: "Neural abstractive summarizers write fluent text, but they often choose the wrong content."
authors: ["Sebastian Gehrmann", "Yuntian Deng", "Alexander Rush"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1443/"
doi: "10.18653/v1/d18-1443"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/gehrmann-2018-bottom-up-abstractive-summarization/"
---
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# Bottom-Up Abstractive Summarization

**Authors:** [Sebastian Gehrmann](https://md.research.elamur.ai/free/en/authors/sebastian-gehrmann/), [Yuntian Deng](https://md.research.elamur.ai/free/en/authors/yuntian-deng/), [Alexander Rush](https://md.research.elamur.ai/free/en/authors/alexander-rush/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1443/) · [PDF](https://aclanthology.org/D18-1443.pdf) · [DOI 10.18653/v1/d18-1443](https://doi.org/10.18653/v1/d18-1443) · [OpenAlex](https://openalex.org/W2889518897)

**Citations:** 667 citations (openalex-snapshot · 2026-10-14) · [272](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Neural abstractive summarizers write fluent text, but they often choose the wrong content. Pointer-generator models in particular tend to copy whole source sentences. Gehrmann, Deng and Rush address this with a two-stage design inspired by bottom-up attention in image captioning.

First, a separate content selector labels each source token by whether it is likely to appear in the summary. The selector is a bidirectional LSTM tagger built on GloVe and ELMo embeddings. Its training labels come from automatically aligning reference summaries to their documents. At decoding time, the selector's output acts as a mask: a standard pointer-generator may copy only selected tokens, though it can still generate any word.

This simple, MLE-trained pipeline beats the See et al. (2017) baseline on CNN-DM and NYT. It matches or exceeds reinforcement-learning systems, and it outperforms end-to-end variants that use multi-task learning or a differentiable mask. Because the selector needs very little data, a summarizer can be adapted to a new domain cheaply, which matters for low-resource settings.

## Key points
- On CNN-DM, ROUGE-L rises from 36.4 (pointer-generator baseline) to 38.3. Overall gains exceed two ROUGE points on both CNN-DM and NYT.
- The content selector reaches over 60% recall and over 50% precision. It can be trained on less than 1% of the original data.
- In domain transfer, a CNN-DM model evaluated on NYT improves by more than 5 ROUGE-L points with a selector trained on only 1,000 in-domain sentences.
- Copying makes outputs highly extractive. Novel words make up 14.8% of reference summaries, 2.2% of pointer-generator output and about 0.5% with bottom-up attention. The mask breaks up long copied spans of 11+ words.
- Three inference-time penalties (length, coverage, trigram repeat) each improve all three ROUGE scores, even when stacked on the other two.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

272 papers of our library cite it (of the 140884 whose bibliographies we read); 29 of them have a page here, 243 not yet.

- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/)
- [Re-evaluating Evaluation in Text Summarization](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/)
- [SummScreen: A Dataset for Abstractive Screenplay Summarization](https://md.research.elamur.ai/free/en/papers/chen-2022-summscreen-dataset-abstractive-screenplay-summarization/)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
- [FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/)
- [Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://md.research.elamur.ai/free/en/papers/fabbri-2019-multi-news-large-scale-multi/)
- [Ranking Generated Summaries by Correctness: An Interesting but Challenging Application for Natural Language Inference](https://md.research.elamur.ai/free/en/papers/falke-2019-ranking-generated-summaries-correctness-interesting/)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://md.research.elamur.ai/free/en/papers/he-2022-ctrlsum-generic-controllable-text-summarization/)
- [Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/)
- [Neural Text Summarization: A Critical Evaluation](https://md.research.elamur.ai/free/en/papers/kryscinski-2019-neural-text-summarization-critical-evaluation/)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/ladhak-2020-wikilingua-new-benchmark-dataset-cross/)
- [Hierarchical Transformers for Multi-Document Summarization](https://md.research.elamur.ai/free/en/papers/liu-2019-hierarchical-transformers-multi-document-summarization/)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/)
- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://md.research.elamur.ai/free/en/papers/pilault-2020-extractive-abstractive-neural-document-summarization/)
- [ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/)
- [Answers Unite! Unsupervised Metrics for Reinforced Summarization Models](https://md.research.elamur.ai/free/en/papers/scialom-2019-answers-unite-unsupervised-metrics-reinforced/)
- [ALFWorld: Aligning Text and Embodied Environments for Interactive Learning](https://md.research.elamur.ai/free/en/papers/shridhar-2020-alfworld-aligning-text-embodied-environments/)
- [Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/)
- [Heterogeneous Graph Neural Networks for Extractive Document Summarization](https://md.research.elamur.ai/free/en/papers/wang-2020-heterogeneous-graph-neural-networks-extractive/)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
- [PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization](https://md.research.elamur.ai/free/en/papers/xiao-2022-primera-pyramid-based-masked-sentence/)
- [Discourse-Aware Neural Extractive Text Summarization](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/)
- [FUDGE: Controlled Text Generation With Future Discriminators](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
- [BARTScore: Evaluating Generated Text as Text Generation](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
- [Big Bird: Transformers for Longer Sequences](https://md.research.elamur.ai/free/en/papers/zaheer-2020-big-bird-transformers-longer-sequences/)
- [HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization](https://md.research.elamur.ai/free/en/papers/zhang-2019-hibert-document-level-pre-training/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{gehrmann2018bottom,
  title = {{Bottom-Up Abstractive Summarization}},
  author = {Gehrmann, Sebastian and Deng, Yuntian and Rush, Alexander},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1443},
  url = {https://doi.org/10.18653/v1/d18-1443}
}
```

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