HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles

• URL: http://arxiv.org/abs/2110.03179v2
• Date: Fri, 8 Oct 2021 19:39:29 GMT
• Title: HowSumm: A Multi-Document Summarization Dataset Derived from WikiHow Articles
• Authors: Odellia Boni, Guy Feigenblat, Guy Lev, Michal Shmueli-Scheuer, Benjamin Sznajder, David Konopnicki
• Abstract summary: We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS) This use-case is different from the use-cases covered in existing multi-document summarization (MDS) datasets and is applicable to educational and industrial scenarios. We describe the creation of the dataset and discuss the unique features that distinguish it from other summarization corpora.
• Score: 8.53502615629675
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present HowSumm, a novel large-scale dataset for the task of query-focused multi-document summarization (qMDS), which targets the use-case of generating actionable instructions from a set of sources. This use-case is different from the use-cases covered in existing multi-document summarization (MDS) datasets and is applicable to educational and industrial scenarios. We employed automatic methods, and leveraged statistics from existing human-crafted qMDS datasets, to create HowSumm from wikiHow website articles and the sources they cite. We describe the creation of the dataset and discuss the unique features that distinguish it from other summarization corpora. Automatic and human evaluations of both extractive and abstractive summarization models on the dataset reveal that there is room for improvement.

Related papers

• CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation [51.2289822267563]
  We propose Corpus Retrieval and Augmentation for Fine-Tuning (CRAFT), a method for generating synthetic datasets. We use large-scale public web-crawled corpora and similarity-based document retrieval to find other relevant human-written documents. We demonstrate that CRAFT can efficiently generate large-scale task-specific training datasets for four diverse tasks.
  arXiv  Detail & Related papers  (2024-09-03T17:54:40Z)
• The Power of Summary-Source Alignments [62.76959473193149]
  Multi-document summarization (MDS) is a challenging task, often decomposed to subtasks of salience and redundancy detection. alignment of corresponding sentences between a reference summary and its source documents has been leveraged to generate training data. This paper proposes extending the summary-source alignment framework by applying it at the more fine-grained proposition span level.
  arXiv  Detail & Related papers  (2024-06-02T19:35:19Z)
• Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles [136.84278943588652]
  We propose a new task of summarizing diverse information encountered in multiple news articles encompassing the same event. To facilitate this task, we outlined a data collection schema for identifying diverse information and curated a dataset named DiverseSumm. The dataset includes 245 news stories, with each story comprising 10 news articles and paired with a human-validated reference.
  arXiv  Detail & Related papers  (2023-09-17T20:28:17Z)
• UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization [54.59104881168188]
  textscUniSumm is a unified few-shot summarization model pre-trained with multiple summarization tasks. textscSummZoo is a new benchmark to better evaluate few-shot summarizers.
  arXiv  Detail & Related papers  (2022-11-17T18:54:47Z)
• How "Multi" is Multi-Document Summarization? [15.574673241564932]
  It is expected that both reference summaries in MDS datasets, as well as system summaries, would indeed be based on dispersed information. We propose an automated measure for evaluating the degree to which a summary is disperse'' Our results show that certain MDS datasets barely require combining information from multiple documents, where a single document often covers the full summary content.
  arXiv  Detail & Related papers  (2022-10-23T10:20:09Z)
• Data Augmentation for Abstractive Query-Focused Multi-Document Summarization [129.96147867496205]
  We present two QMDS training datasets, which we construct using two data augmentation methods. These two datasets have complementary properties, i.e., QMDSCNN has real summaries but queries are simulated, while QMDSIR has real queries but simulated summaries. We build end-to-end neural network models on the combined datasets that yield new state-of-the-art transfer results on DUC datasets.
  arXiv  Detail & Related papers  (2021-03-02T16:57:01Z)
• WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization [16.048329028104643]
  In the Query Focused Multi-Document Summarization (QF-MDS) task, a set of documents and a query are given where the goal is to generate a summary from these documents. One major challenge for this task is the lack of availability of labeled training datasets. We propose a novel weakly supervised learning approach via utilizing distant supervision.
  arXiv  Detail & Related papers  (2020-11-03T02:02:55Z)
• AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization [17.098075160558576]
  We propose a scalable approach called AQuaMuSe to automatically mine qMDS examples from question answering datasets and large document corpora. We publicly release a specific instance of an AQuaMuSe dataset with 5,519 query-based summaries, each associated with an average of 6 input documents selected from an index of 355M documents from Common Crawl.
  arXiv  Detail & Related papers  (2020-10-23T22:38:18Z)
• SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy [92.5683788430012]
  SupMMD is a novel technique for generic and update summarization based on the maximum discrepancy from kernel two-sample testing. We show the efficacy of SupMMD in both generic and update summarization tasks by meeting or exceeding the current state-of-the-art on the DUC-2004 and TAC-2009 datasets.
  arXiv  Detail & Related papers  (2020-10-06T09:26:55Z)
• A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal [10.553314461761968]
  Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries. This work presents a new dataset for MDS that is large both in the total number of document clusters and in the size of individual clusters.
  arXiv  Detail & Related papers  (2020-05-20T14:33:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.