Unsupervised Extractive Summarization using Pointwise Mutual Information (2021.eacl-main)
Copied to clipboard
| Challenge: | Unsupervised approaches to extractive summarization rely on notion of sentence importance defined by semantic similarity between a sentence and the document. |
| Approach: | They propose a method to measure relevance and redundancy using PMI between sentences. |
| Outcome: | The proposed method outperforms similarity-based methods on news, medical journal articles, and personal anecdotes. |
Similar Papers
Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction (2020.acl-main)
Copied to clipboard
| 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. |
Improving Unsupervised Extractive Summarization with Facet-Aware Modeling (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing extractive summarization methods tend to select sentences within the same facet, which leads to facet bias. |
| Approach: | They propose a facet-aware centrality-based ranking model that gives a weight to the sentence centrality score. |
| Outcome: | The proposed method outperforms baseline models on a wide range of summarization tasks and performs comparably to other models. |
A Set Prediction Network For Extractive Summarization (2023.findings-acl)
Copied to clipboard
| Challenge: | Recent approaches to extracting salient sentences from source document are naive and lack dependencies between sentences. |
| Approach: | They propose a set prediction network to detect redundancy relationship between sentences . they use a non-autoregressive decoder to predict sentences in parallel . |
| Outcome: | The proposed method outperforms previous state-of-the-art models on extracted summary datasets. |
BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to extractive and abstractive summarization rely on large-scale parallel corpora of input text and output summaries for direct supervision. |
| Approach: | They propose an unsupervised approach to sentence summarization using the Information Bottleneck principle. |
| Outcome: | The proposed method outperforms unsupervised models on automatic metrics and human evaluation along multiple attributes. |
Simple Unsupervised Summarization by Contextual Matching (P19-1)
Copied to clipboard
| Challenge: | Existing methods for sentence summarization require a large amount of parallel data for supervision to work. |
| Approach: | They propose an unsupervised method for sentence summarization using only language modeling. |
| Outcome: | The proposed method maintains continuous contextual matching while maintaining output fluency without any paired examples. |
Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to rank sentences using pre-trained embeddings create a gap due to different optimization objectives. |
| Approach: | They propose a pre-trained embedding process that optimizes informative sentences . they use sentence-word bipartite graphs to model intra-sentential distinctive features . |
| Outcome: | The proposed model outperforms heavy BERT- or RoBERTa-based sentence ranking methods by providing summary-worthy representations. |
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies on redundancy are focused on salience alone. |
| Approach: | They propose to combine salience and novelty to score redundancy in extractive summarization systems . they also propose to balance saliance and redundancies by scoring redundants first . |
| Outcome: | Empirical results show that AREDSUM-CTX scores salience first, then learns to balance saliency and redundancy. |
Enhancing Extractive Text Summarization with Topic-Aware Graph Neural Networks (2020.coling-main)
Copied to clipboard
| 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. |
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
| Outcome: | The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions. |
Frame Semantic-Enhanced Sentence Modeling for Sentence-level Extractive Text Summarization (2021.emnlp-main)
Copied to clipboard
| Challenge: | Sentence-level extractive text summarization is difficult to model the importance of sentences. |
| Approach: | They propose a Frame Semantic-Enhanced Sentence Modeling for Extractive Summarization that leverages Frame semantics to model sentences from both intra-sentence level and inter-sentent level. |
| Outcome: | The proposed model outperforms six state-of-the-art methods on two benchmark corpus datasets. |