Papers with Automatic summarization
Not all Hallucinations are Good to Throw Away When it Comes to Legal Abstractive Summarization (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing models for summarization of legal documents rely on external knowledge to generate abstracts. |
| Approach: | They propose an entity-driven approach that learns the model to generate factual hallucinations . they evaluate legal documents in English and French to evaluate their results . |
| Outcome: | The proposed approach reduces non-factual hallucinations and maximizes summary coverage and factual hallucines at entity-level. |
Towards Zero-Shot Conditional Summarization with Adaptive Multi-Task Fine-Tuning (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for automatic summarization are limited to providing general-purpose summaries . ad-hoc nature of the task can cause arbitrary summarizing, causing a problem . |
| Approach: | They propose to use multi-task fine-tuning to enable conditional summarization on five tasks . they propose to combine two novel "online" or adaptive task-mixing strategies . |
| Outcome: | The proposed method improves zero-shot conditional summarization quality on five tasks. |
MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments (2023.emnlp-main)
Copied to clipboard
| Challenge: | In the context of the Indian judiciary, there is an additional complexity - Indian legal case judgments are mostly written in complex English due to historical reasons, but a significant portion of India's population lacks a strong command of the English language. |
| Approach: | They propose to summarize Indian legal case judgments in English and Hindi by combining the summaries of 3,122 case judgment from Indian courts into one dataset. |
| Outcome: | The proposed dataset compares the summarization methods with other datasets and shows that the proposed approaches perform better than previous approaches. |
The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)
Copied to clipboard
| Challenge: | Summarization is the task of shortening a text while preserving the most important information it contains. |
| Approach: | They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution. |
| Outcome: | The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work. |
DACSA: A large-scale Dataset for Automatic summarization of Catalan and Spanish newspaper Articles (2022.naacl-main)
Copied to clipboard
| Challenge: | a large corpus of documents is available for summarization tasks in English . supervised methods require adequate corpora for summarizing . |
| Approach: | They describe a corpus of catalan and spanish newspapers that can be used to train summarization models for Catalan, Spanish and other languages. |
| Outcome: | The proposed corpus can be used to train summarization models for Catalan and Spanish. |
Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method (2023.acl-long)
Copied to clipboard
| Challenge: | Experimental results show that automatic summarization generates concise summaries that contain key ideas of source documents. |
| Approach: | They propose to use Element-aware test sets to annotate news-related reference summaries to focus on more fine-grained news elements objectively and comprehensively. |
| Outcome: | The proposed method outperforms state-of-the-art fine-tuned PLMs and zero-shot LLMs by +4.33/+4.77 on the two datasets, respectively. |
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)
Copied to clipboard
| 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. |
GUMSum: Multi-Genre Data and Evaluation for English Abstractive Summarization (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing datasets are limited to newswire text, which is a fraction of extant genres in general and on the Web. |
| Approach: | They present a small but carefully crafted dataset of English summaries in 12 written and spoken genres for evaluation of abstractive summarization. |
| Outcome: | The proposed dataset of English summaries in 12 written and spoken genres is compared with human outputs and compared to untuned and prompt-based approaches. |
Inducing Document Structure for Aspect-based Summarization (P19-1)
Copied to clipboard
| 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. |