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.

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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.
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)

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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.
Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)

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Challenge: Existing methods for extractive and abstractive summarization are far from human performance.
Approach: They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context.
Outcome: The proposed model outperforms previous models on ROUGE-1, ROUGEE-2 and METEOR scores on two datasets of scientific papers.
On the Abstractiveness of Neural Document Summarization (D18-1)

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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.
Neural Extractive Text Summarization with Syntactic Compression (D19-1)

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Challenge: Recent approaches to summarization are either selection-based extraction or generation-based abstraction.
Approach: They propose a neural model for single-document summarization based on joint extraction and syntactic compression.
Outcome: The proposed model outperforms an off-the-shelf compression module and its output generally remains grammatical.
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 .
Content Selection in Deep Learning Models of Summarization (D18-1)

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Challenge: Using deep learning models, we find that word embedding does not improve performance over simpler models.
Approach: They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction .
Outcome: The proposed models improve performance across news, personal stories, meetings, and medical articles.
Inducing Document Structure for Aspect-based Summarization (P19-1)

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Challenge: Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document.
Approach: They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective .
Outcome: The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries.
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)

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Challenge: Existing extractive summarization models hardly capture inter-sentence relationships, especially in long documents.
Approach: They propose to use a graph neural network to capture inter-sentence relationships efficiently via graph-structured document representation.
Outcome: The proposed model outperforms existing models on CNN/DM and NYT datasets and significantly outperfies them on longer documents.
Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)

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Challenge: Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document.
Approach: They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model.
Outcome: The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.

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