# ASPECTNEWS: Aspect-Oriented Summarization of News Documents

arXiv: https://arxiv.org/abs/2110.08296 · HTML: https://mlsanity.com/paper/2110.08296/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Extractive Opinion Summarization in Quantized Transformer Spaces](https://mlsanity.com/paper/2012.04443/) (2020)
- [Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised](https://mlsanity.com/paper/1808.08858/) (2018)
- [Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints](https://mlsanity.com/paper/1603.08887/) (2016)
- [Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach](https://mlsanity.com/paper/2010.06792/) (2020)

## Built on by (most cited first, 30 of 37)

- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [State-of-the-art generalisation research in NLP: A taxonomy and review](https://mlsanity.com/paper/2210.03050/) (2022)
- [Prompted Opinion Summarization with GPT-3.5](https://mlsanity.com/paper/2211.15914/) (2022)
- [OASum: Large-Scale Open Domain Aspect-based Summarization](https://mlsanity.com/paper/2212.09233/) (2022)
- [Controllable Text Summarization: Unraveling Challenges, Approaches, and Prospects -- A Survey](https://mlsanity.com/paper/2311.09212/) (2023)
- [Abstractive Text Summarization: State of the Art, Challenges, and Improvements](https://mlsanity.com/paper/2409.02413/) (2024)
- [OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization](https://mlsanity.com/paper/2312.04440/) (2023)
- [Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets](https://mlsanity.com/paper/2310.13340/) (2023)
- [LexAbSumm: Aspect-based Summarization of Legal Decisions](https://mlsanity.com/paper/2404.00594/) (2024)
- [ArxivDIGESTables: Synthesizing Scientific Literature into Tables using Language Models](https://mlsanity.com/paper/2410.22360/) (2024)
- [Concise Answers to Complex Questions: Summarization of Long-form Answers](https://mlsanity.com/paper/2305.19271/) (2023)
- [DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization](https://mlsanity.com/paper/2212.10018/) (2022)
- [Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals?](https://mlsanity.com/paper/2401.10415/) (2024)
- [arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation](https://mlsanity.com/paper/2504.10284/) (2025)
- [Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts](https://mlsanity.com/paper/2210.12531/) (2022)
- [Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts](https://mlsanity.com/paper/2402.10554/) (2024)
- [Topic-Selective Graph Network for Topic-Focused Summarization](https://mlsanity.com/paper/2302.13106/) (2023)
- [PromptSum: Parameter-Efficient Controllable Abstractive Summarization](https://mlsanity.com/paper/2308.03117/) (2023)
- [STRUM-LLM: Attributed and Structured Contrastive Summarization](https://mlsanity.com/paper/2403.19710/) (2024)
- [AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference](https://mlsanity.com/paper/2410.00409/) (2024)
- [IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization](https://mlsanity.com/paper/2305.11592/) (2023)
- [MODABS: Multi-Objective Learning for Dynamic Aspect-Based Summarization](https://mlsanity.com/paper/2406.03479/) (2024)
- [Aspect-Based Summarization with Self-Aspect Retrieval Enhanced Generation](https://mlsanity.com/paper/2504.13054/) (2025)
- [Combining Deep Neural Reranking and Unsupervised Extraction for Multi-Query Focused Summarization](https://mlsanity.com/paper/2302.01148/) (2023)
- [SummHelper: Collaborative Human-Computer Summarization](https://mlsanity.com/paper/2308.08363/) (2023)
- [Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search](https://mlsanity.com/paper/2509.26435/) (2025)
- [Text Simplification of College Admissions Instructions: A Professionally Simplified and Verified Corpus](https://mlsanity.com/paper/2209.04529/) (2022)
- [ATSumm: Auxiliary information enhanced approach for abstractive disaster Tweet Summarization with sparse training data](https://mlsanity.com/paper/2405.06541/) (2024)
- [Exploring Iterative Controllable Summarization with Large Language Models](https://mlsanity.com/paper/2411.12460/) (2024)
- [Efficient Aspect-Based Summarization of Climate Change Reports with Small Language Models](https://mlsanity.com/paper/2411.14272/) (2024)

## Abstract (arXiv)

Generic summaries try to cover an entire document and query-based summaries try to answer document-specific questions. But real users' needs often fall in between these extremes and correspond to aspects, high-level topics discussed among similar types of documents. In this paper, we collect a dataset of realistic aspect-oriented summaries, AspectNews, which covers different subtopics about articles in news sub-domains. We annotate data across two domains of articles, earthquakes and fraud investigations, where each article is annotated with two distinct summaries focusing on different aspects for each domain. A system producing a single generic summary cannot concisely satisfy both aspects. Our focus in evaluation is how well existing techniques can generalize to these domains without seeing in-domain training data, so we turn to techniques to construct synthetic training data that have been used in query-focused summarization work. We compare several training schemes that differ in how strongly keywords are used and how oracle summaries are extracted. Our evaluation shows that our final approach yields (a) focused summaries, better than those from a generic summarization system or from keyword matching; (b) a system sensitive to the choice of keywords.