Challenge: Abstractive summarization models have made great strides in recent years, but little is known about how they actually form summaries and how to understand where their decisions come from.
Approach: They propose a two-step method to interpret summarization model decisions by categorizing each decoder decision into one of several generation modes.
Outcome: The proposed method can identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, and study complex generation phenomena on a per-instance basis.

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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.
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.
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.
A Cascade Approach to Neural Abstractive Summarization with Content Selection and Fusion (2020.aacl-main)

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Challenge: Existing systems that perform content selection and surface realization are not able to provide sufficient training data for news summarization.
Approach: They propose to use a cascade architecture to perform content selection and surface realization together to generate abstracts.
Outcome: The proposed architecture outperforms or outranks existing systems in terms of content selection and surface realization.
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.
Training Dynamics for Text Summarization Models (2022.findings-acl)

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Challenge: Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets.
Approach: They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process .
Outcome: The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens .
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 .
Inference Time Style Control for Summarization (2021.naacl-main)

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Challenge: Existing methods to generate summaries of different styles without training separate models are lacking parallel data and expensive (re)training.
Approach: They propose two methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model.
Outcome: The proposed methods generate news headlines with various ideological leanings while still informative.
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.
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.

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