Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model

By Alexander R. Fabbri, Irene Z Li, Tianwei She, Suyi Li, Dragomir R. Radev. 2019

732 citations recorded by the source corpus.

Automatic generation of summaries from multiple news articles is a valuable tool as the number of online publications grows rapidly. Single document summarization (SDS) systems have benefited from advances in neural encoder-decoder model thanks to the availability of large datasets. However, multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. In this paper, we introduce Multi-News, the first large-scale MDS news dataset. Additionally, we propose an end-to-end model which incorporates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. We benchmark several methods on Multi-News and hope that this work will promote advances in summarization in the multi-document setting.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders (2019, background): “…weather to be the coolest and fun day and he went to the beach and got ready. primarily from the news domain, abstractive summarization has been approached as a supervised neural sequence transduction problem (Rush et al., 2015; Nallapati et al., 2016; Narayan et al., 2018; Fabbri et al., 2019).”
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers (2021, background): “, 2017) and MultiNews (Fabbri et al., 2019)), along with 3 text classification tasks (civil comments (Borkan et al.”
Finetuned Language Models Are Zero-Shot Learners (2021, neutral): “MultiNews (Fabbri et al., 2019) 5.”
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (2023, methodology): “Data We evaluate on summarization datasets CNN/Daily Mail (Nallapati et al., 2016), arXiv and PubMed (Cohan et al., 2018), MediaSum (Zhu et al., 2021), and Multi-News (Fabbri et al., 2019); (Google, 2020).”
UL2: Unifying Language Learning Paradigms (2022, methodology): “We use CNN/Dailymail (Hermann et al., 2015), XSUM (Narayan et al., 2018), MultiNews (Fabbri et al., 2019), SAMSum (Gliwa et al., 2019), WebNLG (Castro Ferreira et al., 2020) (English), E2E (Dušek et al., 2019) and CommonGen (Lin et al., 2020) to evaluate our models.”
LongForm: Effective Instruction Tuning with Reverse Instructions (2023, neutral): “7B, despite both having similar number of parameters, which aligns with findings from Touvron et al. (2023a).”
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP (2021, neutral): “…Zhou et al. 2019 medical_questions_pairs cls/paraphrase McCreery et al. 2020 mocha other/regression Chen et al. 2020a multi_news cg/summarization Fabbri et al. 2019 numer_sense qa/closed-book qa Lin et al. 2020a onestop_english cls/other Vajjala and Lučić 2018 openbookqa qa/multiple-choice qa…”
Factual Error Correction for Abstractive Summarization Models (2020, background): “Self-supervised methods have achieved success in a wide range of NLP tasks, and automatic summarization is no exception (Liu and Lapata, 2019; Lewis et al., 2019; Zhang et al., 2019a; Shi et al., 2019; Fabbri et al., 2019).”
Multi-document Summarization via Deep Learning Techniques: A Survey (2020, methodology): “Alternatively, in the news domain, Multi-news [35] is a large-scale dataset containing various topics for multi-document summarization.”
LongT5: Efficient Text-To-Text Transformer for Long Sequences (2021, background): “Multi-News (Fabbri et al., 2019) The task involves summarizing multiple news documents about a topic into a human-written summary.”
Data Augmentation for Abstractive Query-Focused Multi-Document Summarization (2021, background): “Recently, multiple new MDS datasets have been introduced: A large-scale dataset by Liu et al. (2018) named WikiSum, and a smaller MDS by Fabbri et al. (2019).”
Editing Models with Task Arithmetic (2022, unknown): “[24]), question generation (SQuAD, Rajpurkar et al.”
SMART: Sentences as Basic Units for Text Evaluation (2022, methodology): “We show that SMART with BLEURT (Sellam et al., 2020) as a soft-matching function outperforms all the approaches we compared against on all four dimensions of quality in the SummEval dataset (Fabbri et al., 2019).”
Efficient Streaming Language Models with Attention Sinks (2023, unknown): “(2020)), and summarization (GovReport (Huang et al., 2021), MultiNews (Fabbri et al., 2019)).”