What is this Article about? Extreme Summarization with Topic-aware Convolutional Neural Networks
원문 초록
We introduce 'extreme summarization', a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question ``What is the article about?''. We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News Articles
A MISMATCHED Benchmark for Scientific Natural Language Inference
20min-XD: A Comparable Corpus of Swiss News Articles
ARC: Argument Representation and Coverage Analysis for Zero-Shot Long Document Summarization with Instruction Following LLMs
SCIRGC: Multi-Granularity Citation Recommendation and Citation Sentence Preference Alignment
Extractive Multi-document Summarization using K-means, Centroid-based Method, MMR, and Sentence Position
토론
이 논문과 연결된 포럼 글이 아직 없습니다.