Papers with extractive methods
Abstractive Timeline Summarization (D19-54)
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| Challenge: | Prior approaches to TLS focus on extractive methods, which generate extractive timelines . a study with human judges shows that our abstractive system also produces output that is easy to read and understand. |
| Approach: | They propose an abstractive timeline summarization system that is unsupervised . their system outperforms extractive systems in terms of ROUGE scores . |
| Outcome: | The proposed system outperforms extractive systems in terms of ROUGE scores . it produces output that is easy to read and understand, the authors say . |
BillSum: A Corpus for Automatic Summarization of US Legislation (D19-54)
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| Challenge: | In the US Congress, over 10,000 bills are introduced each year, with state legislatures introducing tens of thousands of bills. |
| Approach: | They introduce the first dataset for summarizing US Congressional and California state bills . they demonstrate that models built on Congressional bills can be used to summarize California billa . |
| Outcome: | The proposed summarization methods can be applied to states without human-written summaries. |
STRASS: A Light and Effective Method for Extractive Summarization Based on Sentence Embeddings (P19-2)
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| Challenge: | Summarization is a costly and timedemanding task. |
| Approach: | They propose an extractive text summarization method which leverages the semantic information in existing sentence embedding spaces. |
| Outcome: | The proposed method performs similarly to state-of-the-art extractive methods with effective training and inference time. |
Tailoring Rumor Debunking to You: Diversifying Chinese Rumor-Debunking Passages with an LLM-Driven Simulated Feedback-Enhanced Framework (2026.eacl-industry)
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| Challenge: | Existing methods for fact-checking lack coherence and context, whereas abstractive methods lack cohesion and context. |
| Approach: | They propose a framework that generates Chinese user-specific debunking passages . they propose to use a generative AI framework to generate context-sensitive responses . |
| Outcome: | The proposed framework generates Chinese user-specific debunking passages by iteratively refining outputs based on simulated user feedback. |
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Current retrieval-augmented generation systems struggle when retrieval models fail to rank the most relevant documents . existing extractive methods reduce latency but rely on independent, non-adaptive sentence selection . |
| Approach: | They introduce an extractive context compression framework that enhances retrieval-augmented generation in question answering. |
| Outcome: | EXIT surpasses existing compression methods and uncompressed baselines in QA accuracy . the framework reduces inference time and token count while preserving contextual dependencies . |
Reading Like HER: Human Reading Inspired Extractive Summarization (D19-1)
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| Challenge: | Existing methods for extracting text summarization are abstractive and extractive. |
| Approach: | They propose a novel approach for extractive summarization by simulating two stages . they adopt a convolutional neural network to encode gist of paragraphs for rough reading . |
| Outcome: | The proposed method significantly outperforms the state-of-the-art extractive methods on CNN and DailyMail datasets. |
Few-Shot Learning for Opinion Summarization (2020.emnlp-main)
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| Challenge: | a recent study shows that abstractive summarization models fail to capture their essential properties due to the high cost of summary production. |
| Approach: | They propose a few-shot framework for abstractive opinion summarization that bootstraps the output of an unsupervised model. |
| Outcome: | The proposed framework outperforms extractive and abstractive methods on Amazon and Yelp datasets. |
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (P19-1)
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| Challenge: | Existing studies on commonsense knowledge base construction only store loosely structured open-text descriptions of knowledge. |
| Approach: | They propose a commonsense knowledge base construction model that generates rich commonsensense descriptions in natural language. |
| Outcome: | The proposed models can generate rich and diverse commonsense descriptions in natural language. |
On Extractive and Abstractive Neural Document Summarization with Transformer Language Models (2020.emnlp-main)
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| Challenge: | We present a method to produce abstractive summaries of documents that exceed several thousand words . we compare transformer based methods to extractive methods, but extractive models score higher . |
| Approach: | They propose a method to generate abstractive summaries of documents that exceed several thousand words via neural abstractive summary. |
| Outcome: | The proposed method produces abstractive summaries of documents that exceed several thousand words . it is compared with baseline methods, state-of-the-art models and variants of the proposed method . |
Pay Attention to Implicit Attribute Values: A Multi-modal Generative Framework for AVE Task (2023.findings-acl)
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Yupeng Zhang, Shensi Wang, Peiguang Li, Guanting Dong, Sirui Wang, Yunsen Xian, Zhoujun Li, Hongzhi Zhang
| Challenge: | Existing approaches to extract attribute values from product descriptions are incomplete and noisy due to the tedious nature of this task. |
| Approach: | They propose a framework to extract attributes from product descriptions to acquire implicit attributes in addition to the explicit ones. |
| Outcome: | The proposed framework outperforms existing methods on the extraction of implicit attribute values while achieving comparable performance for the explicit ones. |