# SumREN: Summarizing Reported Speech about Events in News

arXiv: https://arxiv.org/abs/2212.01146 · HTML: https://mlsanity.com/paper/2212.01146/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 54 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 30 of 30)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [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)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://mlsanity.com/paper/1906.01749/) (2019)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics](https://mlsanity.com/paper/2104.13346/) (2021)
- [Generating Datasets with Pretrained Language Models](https://mlsanity.com/paper/2104.07540/) (2021)
- [Entity-level Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/2102.09130/) (2021)
- [ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking](https://mlsanity.com/paper/2207.04108/) (2022)
- [Exploring Neural Models for Query-Focused Summarization](https://mlsanity.com/paper/2112.07637/) (2021)
- [AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization](https://mlsanity.com/paper/2010.12694/) (2020)
- [Generating Representative Headlines for News Stories](https://mlsanity.com/paper/2001.09386/) (2020)
- [Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization](https://mlsanity.com/paper/1908.11723/) (2019)
- [Generating Query Focused Summaries from Query-Free Resources](https://mlsanity.com/paper/2012.14774/) (2020)
- [Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline](https://mlsanity.com/paper/2009.00590/) (2020)
- [Sentiment Analysis in the News](https://mlsanity.com/paper/1309.6202/) (2013)
- [Controllable Summarization with Constrained Markov Decision Process](https://mlsanity.com/paper/2108.03405/) (2021)
- [Proposition-Level Clustering for Multi-Document Summarization](https://mlsanity.com/paper/2112.08770/) (2021)
- [DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles](https://mlsanity.com/paper/2110.07827/) (2021)
- [End-to-end Neural Coreference Resolution Revisited: A Simple yet Effective Baseline](https://mlsanity.com/paper/2107.01700/) (2021)

## Built on by (most cited first, 4 of 4)

- [SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts](https://mlsanity.com/paper/2303.14337/) (2023)
- [Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization](https://mlsanity.com/paper/2305.01951/) (2023)
- [Interpretable Automatic Fine-grained Inconsistency Detection in Text Summarization](https://mlsanity.com/paper/2305.14548/) (2023)
- [FRACAS: A FRench Annotated Corpus of Attribution relations in newS](https://mlsanity.com/paper/2309.10604/) (2023)

## Abstract (arXiv)

A primary objective of news articles is to establish the factual record for an event, frequently achieved by conveying both the details of the specified event (i.e., the 5 Ws; Who, What, Where, When and Why regarding the event) and how people reacted to it (i.e., reported statements). However, existing work on news summarization almost exclusively focuses on the event details. In this work, we propose the novel task of summarizing the reactions of different speakers, as expressed by their reported statements, to a given event. To this end, we create a new multi-document summarization benchmark, SUMREN, comprising 745 summaries of reported statements from various public figures obtained from 633 news articles discussing 132 events. We propose an automatic silver training data generation approach for our task, which helps smaller models like BART achieve GPT-3 level performance on this task. Finally, we introduce a pipeline-based framework for summarizing reported speech, which we empirically show to generate summaries that are more abstractive and factual than baseline query-focused summarization approaches.