| Challenge: | Recent studies show that document summarization systems are abstractive . authors suggest that automated summarizing systems could be improved . |
| Approach: | They propose to use a pure copy system to verify abstractiveness of document summarization systems. |
| Outcome: | The proposed system produces abstractive summaries while being far more efficient. |
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On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
| Approach: | They propose a method to generate abstractive summaries of documents that exceed several thousand words via neural abstractive summary. |
| Outcome: | The proposed method produces abstractive summaries of documents that exceed several thousand words . it is compared with baseline methods, state-of-the-art models and variants of the proposed method . |
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (N19-1)
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| Challenge: | Neural abstractive summarization systems generate summary texts conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarizing datasets. |
| Approach: | They propose to analyze existing neural abstractive summarization systems by comparing their performance to human-written summaries. |
| Outcome: | The proposed systems perform better than human-written summarizations on different datasets and show that they are able to understand deeper syntactic and semantic structures. |
Abstractive Summarizers are Excellent Extractive Summarizers (2023.acl-short)
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| Challenge: | Abstractive summarization systems have traditionally been fragmented, limiting the benefits of compatible models. |
| Approach: | They propose three new inference algorithms using sequence-to-sequence architectures to model extractive summarization with an abstractive summmarization system. |
| Outcome: | The proposed algorithms outperform existing models on CNN and Dailymail and show that they are more efficient than existing models. |
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
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Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
| Approach: | They propose a hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on two large-scale datasets of scientific papers. |
On Faithfulness and Factuality in Abstractive Summarization (2020.acl-main)
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| Challenge: | Existing conditional text generation models produce unfaithful and unfaithed summaries . current models accomplish a high level of fluency and coherence . |
| Approach: | They propose to use pretrained models for document summarization to better understand hallucinations . they find that textual entailment measures better correlate with faithfulness . |
| Outcome: | The proposed models generate faithful and factual summaries as evaluated by humans. |
Guided Neural Language Generation for Abstractive Summarization using Abstract Meaning Representation (D18-1)
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| Challenge: | Recent work on abstractive summarization has made progress with neural encoder-decoder architectures, but these models lack explicit semantic modeling of the source document and its summary. |
| Approach: | They extend previous work on abstractive summarization using Abstract Meaning Representation (AMR) with a neural language generation stage which they guide using the source document. |
| Outcome: | The proposed approach improves summarization performance by 7.4 and 10.5 points in ROUGE-2 using gold standard AMR parses and parses obtained from an off-the-shelf parser respectively. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization are extractive and abstractive. |
| Approach: | They propose to jointly learn an abstractive single-document decoder and a decoding controller to aggregate the decoded outputs for multiple input documents. |
| Outcome: | The proposed model outperforms several baselines on two multi-document summarization datasets and proves that it is useful for both tasks. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
To Point or Not to Point: Understanding How Abstractive Summarizers Paraphrase Text (2021.findings-acl)
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| Challenge: | Abstractive summarization models have seen great improvements in recent years, but there is limited understanding of the strategies different models employ and how they relate their understanding of language. |
| Approach: | They characterize how one popular abstractive model uses an explicit copy/generation switch to control its level of abstraction vs extraction . they find that abstractive summarization models lack the semantic understanding necessary to generate paraphrases that are both abstractive and faithful to the source document. |
| Outcome: | The proposed model uses syntactic boundaries to truncate sentences that are often copied verbatim. |