| Challenge: | Existing approaches to extractive summarization use recurrent neural networks to model document . Existing systems use a vector representation for each sentence to generate a summary . |
| Approach: | They propose a model that induces a multi-root dependency tree while predicting the output summary. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
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StructSum: Summarization via Structured Representations (2021.eacl-main)
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Vidhisha Balachandran, Artidoro Pagnoni, Jay Yoon Lee, Dheeraj Rajagopal, Jaime Carbonell, Yulia Tsvetkov
| 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. |
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. |
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer (2021.emnlp-main)
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| Challenge: | Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. |
| Approach: | They propose a nested tree-based extractive summarization model on RoBERTa that uses syntactic and discourse trees to represent sentences in a given document. |
| Outcome: | The proposed model outperforms baseline models on the CNN/DailyMail dataset and achieves significantly better scores than the baseline models in terms of coherence and comparable scores to the state-of-the-art models. |
Unifying Parsing and Tree-Structured Models for Generating Sentence Semantic Representations (2022.naacl-srw)
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| Challenge: | Existing tree-based models require handannotated data to be trained. |
| Approach: | They propose a tree-based model that learns its composition function together with its structure. |
| Outcome: | The proposed model outperforms existing models on downstream tasks and is competitive with Bert base model. |
DTCRS: Dynamic Tree Construction for Recursive Summarization (2025.acl-long)
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| Challenge: | Recursive summarization (RAG) is an important method for mitigating large model hallucinations and enhancing answer interpretability. |
| Approach: | They propose a method that dynamically generates summary trees based on document structure and query semantics. |
| Outcome: | The proposed method significantly reduces summary tree construction time and achieves substantial improvements across three QA tasks. |
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)
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| Challenge: | Automated summarization has focused on ten to twenty documents, typically news articles, but could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader. |
| Approach: | They propose a method for creating hierarchical summarization corpora from large, heterogeneous document collections by crowdsourcing relevant content and asking trained annotators to order the relevant information hierarchically. |
| Outcome: | The proposed method can be used to develop and evaluate hierarchical summarization systems. |
A Set Prediction Network For Extractive Summarization (2023.findings-acl)
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| 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. |
GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization (2025.naacl-short)
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| Challenge: | Graph-based methods for extracting documents have been popular, but they often require external tools or additional machine learning models to define graph components. |
| Approach: | They propose a heterogeneous graph construction for extractive summarization that defines two levels of information and four types of edges without any need for auxiliary learning models. |
| Outcome: | The proposed graph construction outperforms previous graph-based models on two datasets and is available on GitHub. |
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. |
Abstractive Summarization Guided by Latent Hierarchical Document Structure (2022.emnlp-main)
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| Challenge: | Sequential abstractive summarizations often do not capture hierarchical and inter-sentential dependencies in the summmarized document. |
| Approach: | They propose a hierarchy-aware graph neural network which captures hierarchical and inter-sentential dependencies in the summmarized document. |
| Outcome: | The proposed model improves strong sequence models such as BART with a 0.55 and 0.75 margin in ROUGE-1/2/L for CNN/DM and XSum. |