| Challenge: | Sentence regression is an emerging branch in automatic text summarizations. |
| Approach: | They propose to estimate the importance of information via learned utility scores for individual sentences. |
| Outcome: | The proposed models learn to predict ROUGE recall scores of individual sentences . the models show that following intuition leads to suboptimal results . |
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Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)
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| Challenge: | Sentence summarization systems that use latent space to reconstruct the source sentence are unwillingly exploited. |
| Approach: | They propose a method that uses language modeling and semantic similarity metrics to find a high-scoring summary. |
| Outcome: | The proposed method achieves state-of-the-art for unsupervised sentence summarization according to ROUGE scores. |
Objective Function Learning to Match Human Judgements for Optimization-Based Summarization (N18-2)
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| Challenge: | In previous work on summarization, the objective function is based on ad-hoc assumptions about which quality aspects of a summary are relevant. |
| Approach: | They learn a summary-level scoring function including human judgments as supervision and automatically generated data as regularization. |
| Outcome: | The proposed method performs well across automatic and manual evaluations. |
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. |
PreSumm: Predicting Summarization Performance Without Summarizing (2025.findings-acl)
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| Challenge: | Recent advances in summarization models do not produce all documents in the same way, despite their inherent design principles and operational mechanisms. |
| Approach: | They propose a task where a system predicts summarization performance based solely on the source document. |
| Outcome: | The proposed task identifies documents that require manual summarization and improves dataset quality by filtering outliers and noisy documents. |
Abstractive Document Summarization with Summary-length Prediction (2023.findings-eacl)
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| Challenge: | Existing abstractive summarization models do not consider summarizing-specific information such as the target summary length. |
| Approach: | They propose a method for enabling a model to understand summarization-specific information by predicting the summary length in the encoder and generating a summary of the predicted length in fine-tuning. |
| Outcome: | The proposed method improves ROUGE scores on the WikiHow, NYT, and CNN/DM datasets. |
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. |
Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)
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| 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. |
Assessing Quality Estimation Models for Sentence-Level Prediction (C18-1)
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| Challenge: | Using a relevant QE model is also very important in QE. |
| Approach: | They evaluate a wide range of advanced sentence-level Quality Estimation models including Support Vector Regression, Ride Regression and Bayesian Neural Networks. |
| Outcome: | The proposed models behave differently in evaluation settings depending on whether test data come from the same domain as the training data or not. |
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. |
Align then Summarize: Automatic Alignment Methods for Summarization Corpus Creation (2020.lrec-1)
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| Challenge: | Summarizing text is not a straightforward task. |
| Approach: | They propose to use automated transcriptions to generate reports from automatic transcriptions as a dataset for neural summarization. |
| Outcome: | The proposed model improves on publicmeetings corpus on a dataset of aligned public meetings. |