Challenge: Neural text summarization models are limited by their maximum input length, posing a challenge to summarizing longer texts comprehensively.
Approach: They propose a pre-processing layer that removes low-quality sentences in articles to improve existing summarization models.
Outcome: The proposed approach improves state-of-the-art summarization models on WikiHow and Reddit TIFU datasets by 3.84 and 8.57 points on the full test set and the long article subset.

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What Have We Achieved on Text Summarization? (2020.emnlp-main)

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Challenge: Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals.
Approach: They analyze 8 major sources of errors on 10 representative summarization models manually.
Outcome: Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models.
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.
Neural Text Summarization: A Critical Evaluation (D19-1)

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Challenge: Current approaches to text summarization use advanced attention and copying mechanisms, multi-task and multi-reward training techniques.
Approach: They evaluate datasets, evaluation metrics, and models for text summarization . they highlight three primary shortcomings: 1) datasets leave task underconstrained; 2) models overfit layout biases .
Outcome: The current evaluation protocol is weakly correlated with human judgment and does not account for factual correctness.
Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)

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Challenge: Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries.
Approach: They propose to exploit discourse-level segmentation as a finer-grained means to more precisely pinpoint the core content in a document.
Outcome: The proposed method improves extractive summarization performance on CNN/Daily Mail dataset.
Learning to Rank Salient Content for Query-focused Summarization (2024.emnlp-main)

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Challenge: Query-focused summarization (QFS) is gaining prominence in research community.
Approach: They propose to integrate Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization.
Outcome: The proposed model outperforms the state-of-the-art on QMSum benchmark and SQuALITY benchmark while offering a lower training overhead.
Toward Unifying Text Segmentation and Long Document Summarization (2022.emnlp-main)

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Challenge: Abstractive strategies produce more condensed summaries, but they suffer from hallucinations and factual errors, which pose a more difficult generation challenge.
Approach: They propose a method that learns robust sentence representations by performing summarization and segmentation simultaneously, which is further enhanced by an optimization-based regularizer to promote selection of diverse summary sentences.
Outcome: The proposed model achieves state-of-the-art performance on publicly available benchmarks and better cross-genre transferability when equipped with text segmentation.
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.
Bottom-Up Abstractive Summarization (D18-1)

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Challenge: Existing approaches to summarize text using end-to-end content selectors have had mixed success in content selection, for example copying full sentences from the source document.
Approach: They propose to use content selectors to over-determine phrases in a source document that should be part of the summary.
Outcome: The proposed model over-determines phrases in a source document that should be part of the summary while generating fluent summaries.
A Guide To Effectively Leveraging LLMs for Low-Resource Text Summarization: Data Augmentation and Semi-supervised Approaches (2025.findings-naacl)

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Challenge: Existing approaches for low-resource text summarization use large language models (LLMs) but such models suffer from inconsistent outputs and are difficult to adapt to domain-specific data.
Approach: They propose two methods to effectively utilize large language models for low-resource text summarization.
Outcome: The proposed methods synthesize high-quality documents using LLaMA-3-70b-Instruct model . they achieve competitive ROUGE scores as a fully supervised method with 5% of the labeled data.
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.

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