Challenge: Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores.
Approach: They propose a model that extracts salient sentences from a document and paraphrases them to generate a summary.
Outcome: The proposed model improves on CNN/Daily Mail and New York Times datasets.

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Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)

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Challenge: Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem.
Approach: They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective.
Outcome: The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting (P18-1)

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Challenge: Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores.
Approach: They propose a sentence-level policy gradient method that bridges computation between two neural networks in a hierarchical way while maintaining language fluency.
Outcome: The proposed model achieves state-of-the-art on all metrics and higher abstractiveness scores on the CNN/Daily Mail dataset and faster training convergence than previous models.
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization (2024.lrec-main)

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Challenge: Existing models and datasets for training summarization models are limited for less resourceful languages like Hungarian .
Approach: They propose to use a Hungarian corpus for training abstractive and extractive summarization models by cleaning, preprocessing and deduplication.
Outcome: The proposed model trains abstractive and extractive summarization models using the dataset . it will be made publicly available, encouraging replication, further research, and real-world applications across various domains.
CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization (2022.coling-1)

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Challenge: Existing methods for extractive and abstractive summarization use token-level or sentence-level training objectives.
Approach: They propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo.
Outcome: The proposed framework boosts extractive and abstractive results on CNN/DailyMail benchmarks while maintaining inference efficiency.
Answers Unite! Unsupervised Metrics for Reinforced Summarization Models (D19-1)

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Challenge: Abstractive summarization approaches based on Reinforcement Learning (RL) have been proposed to overcome classical likelihood maximization.
Approach: They propose to use Reinforcement Learning to learn the model parameters through RL techniques to overcome classical likelihood maximization.
Outcome: The proposed measures favor ROUGE with the additional property of not requiring reference summaries.
Abstractive Text Summarization based on Language Model Conditioning and Locality Modeling (2020.lrec-1)

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Challenge: Abstractive summarization is an NLP task with many real-world applications.
Approach: They propose to use a pre-trained language model to train a Transformer-based neural model . they propose a new method of BERT-windowing to allow chunk-wise processing of texts longer than the BERT window size .
Outcome: The proposed model outperforms baseline models on CNN/Daily Mail dataset and shows its superiority on German dataset.
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.
Text Summarization with Pretrained Encoders (D19-1)

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Challenge: Existing pretraining languages such as ELMo and GPT have advanced a wide range of tasks.
Approach: They propose a novel document-level encoder based on BERT which can express the semantics of a document and obtain representations for its sentences.
Outcome: The proposed model achieves state-of-the-art in extractive and abstractive settings.
StructSum: Summarization via Structured Representations (2021.eacl-main)

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Challenge: Abstractive summarization models overfit to training corpora, lack of transparency and layout bias . authors propose incorporating latent and explicit dependencies across sentences in source document .
Approach: They propose a framework based on document-level structure induction to address layout bias and lack of transparency in abstractive summarization models.
Outcome: The proposed framework improves coverage of content in the source documents and generates more abstractive summaries by generating more novel n-grams.
Reinforced Extractive Summarization with Question-Focused Rewards (P18-3)

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Challenge: Existing methods for extractive summarization use human abstracts to create annotations for extraction units.
Approach: They propose a method where abstracts are converted to Cloze-style comprehension questions to generate extractive summarization.
Outcome: The proposed method surpasses state-of-the-art systems on the standard summarization dataset.

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