首页/论文/1906.01351

arXiv 1906.013512019-06-13cs.CL

TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks

IBM Research（IBM研究院）

Guy Lev, Michal Shmueli-Scheuer, Jonathan Herzig, Achiya Jerbi, David Konopnicki

备注：: Accepted to ACL 2019

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

Currently, no large-scale training data is available for the task of scientific paper summarization. In this paper, we propose a novel method that automatically generates summaries for scientific papers, by utilizing videos of talks at scientific conferences. We hypothesize that such talks constitute a coherent and concise description of the papers' content, and can form the basis for good summaries. We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries. A model trained on this dataset achieves similar performance as models trained on a dataset of summaries created manually. In addition, we validated the quality of our summaries by human experts.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

NUTSHELL：用于从科学演讲中生成摘要的数据集

NUTSHELL: A Dataset for Abstract Generation from Scientific Talks

Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Fondazione Bruno Kessler（布鲁诺·凯斯勒基金会）

本文提出NUTSHELL数据集，用于科学演讲摘要生成，通过建立基线模型和评估生成摘要质量，揭示SAG的挑战并促进改进模型和评估方法的发展。

2025-06-02cs.CL

From Arguments to Key Points: Towards Automatic Argument Summarization

IBM Research（IBM研究院）

Generating a concise summary from a large collection of arguments on a given topic is an intriguing yet understudied problem. We propose to represent such summaries as a small set of talking points, termed "key points", each scored according to its salience. We show, by analyzing a large dataset of crowd-contributed arguments, that a small number of key points per topic is typically sufficient for covering the vast majority of the arguments. Furthermore, we found that a domain expert can often predict these key points in advance. We study the task of argument-to-key point mapping, and introduc…

2020-06-09cs.CL

Community-Driven Comprehensive Scientific Paper Summarization: Insight from cvpaper.challenge

Waseda University（早稻田大学）；National Institute of Advanced Industrial Science and Technology (AIST)（产业技术综合研究所）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Waseda Research Institute for Science and Engineering（早稻田大学理工学综合研究所）

The present paper introduces a group activity involving writing summaries of conference proceedings by volunteer participants. The rapid increase in scientific papers is a heavy burden for researchers, especially non-native speakers, who need to survey scientific literature. To alleviate this problem, we organized a group of non-native English speakers to write summaries of papers presented at a computer vision conference to share the knowledge of the papers read by the group. We summarized a total of 2,000 papers presented at the Conference on Computer Vision and Pattern Recognition, a top-ti…

2022-03-17cs.CLcs.CV

那场演讲在讲什么？一个面向科学演示的视频到文本摘要数据集

What Is That Talk About? A Video-to-Text Summarization Dataset for Scientific Presentations

Saarland University（萨尔兰大学）；Max Planck Institute for Informatics（马克斯·普朗克信息研究所）；University of Cambridge（剑桥大学）；University of Edinburgh（爱丁堡大学）

本文提出VISTA数据集，含18,599个AI会议演示及其摘要，并采用基于计划的框架提升摘要质量，但模型与人类表现仍有差距。

2025-05-24cs.AIcs.CLcs.CV

幻灯片有帮助吗？会议演讲自动转录的多模态上下文

Do Slides Help? Multi-modal Context for Automatic Transcription of Conference Talks

Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）

本研究提出利用演示幻灯片作为多模态上下文增强语音识别，通过数据增强训练模型，在科学演讲中词错误率相对降低34%，领域术语降低35%。

2025-10-15cs.AIcs.CL

Spotify at TREC 2020: Genre-Aware Abstractive Podcast Summarization

University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Spotify

This paper contains the description of our submissions to the summarization task of the Podcast Track in TREC (the Text REtrieval Conference) 2020. The goal of this challenge was to generate short, informative summaries that contain the key information present in a podcast episode using automatically generated transcripts of the podcast audio. Since podcasts vary with respect to their genre, topic, and granularity of information, we propose two summarization models that explicitly take genre and named entities into consideration in order to generate summaries appropriate to the style of the po…

2021-04-07cs.CL

更多相关论文收起更多论文

D2S: Document-to-Slide Generation Via Query-Based Text Summarization

University of Michigan（密歇根大学）；IBM Research Europe（IBM欧洲研究院）；IBM Research（IBM研究院）

Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years' NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a t…

2021-05-08cs.CL

Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction

L3S Research Center, Leibniz Universität Hannover（莱布尼茨汉诺威大学L3S研究中心）；Leibniz Information Centre for Science and Technology (TIB)（莱布尼茨科学技术信息中心（TIB））

Effective learning with audiovisual content depends on many factors. Besides the quality of the learning resource's content, it is essential to discover the most relevant and suitable video in order to support the learning process most effectively. Video summarization techniques facilitate this goal by providing a quick overview over the content. It is especially useful for longer recordings such as conference presentations or lectures. In this paper, we present an approach that generates a visual summary of video content based on semantic word embeddings and keyphrase extraction. For this pur…

2019-11-25cs.CLcs.DLcs.MM

WSL-DS: Weakly Supervised Learning with Distant Supervision for Query Focused Multi-Document Abstractive Summarization

York University（约克大学）；Information Retrieval and Knowledge Management Research Lab（信息检索与知识管理研究实验室）；School of Information Technology（信息技术学院）；Department of Electrical Engineering and Computer Science（电子工程与计算机科学系）

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 based on the given query. However, one major challenge for this task is the lack of availability of labeled training datasets. To overcome this issue, in this paper, we propose a novel weakly supervised learning approach via utilizing distant supervision. In particular, we use datasets similar to the target dataset as the training data where we leverage pre-trained sentence similarity models to generate the weak reference summary…

2020-11-03cs.CLcs.IR

MUDOS-NG: Multi-document Summaries Using N-gram Graphs (Tech Report)

NCSR Demokritos（德谟克利特国家科学研究中心）；University of the Aegean（爱琴海大学）

This report describes the MUDOS-NG summarization system, which applies a set of language-independent and generic methods for generating extractive summaries. The proposed methods are mostly combinations of simple operators on a generic character n-gram graph representation of texts. This work defines the set of used operators upon n-gram graphs and proposes using these operators within the multi-document summarization process in such subtasks as document analysis, salient sentence selection, query expansion and redundancy control. Furthermore, a novel chunking methodology is used, together wit…

2010-12-09cs.AIcs.CL

See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization

IIIT-Delhi（印度德里信息技术学院）

In recent years, abstractive text summarization with multimodal inputs has started drawing attention due to its ability to accumulate information from different source modalities and generate a fluent textual summary. However, existing methods use short videos as the visual modality and short summary as the ground-truth, therefore, perform poorly on lengthy videos and long ground-truth summary. Additionally, there exists no benchmark dataset to generalize this task on videos of varying lengths. In this paper, we introduce AVIATE, the first large-scale dataset for abstractive text summarization…

2021-09-15cs.CLcs.LG

Artex is AnotheR TEXt summarizer

Laboratoire Informatique d’Avignon（阿维尼翁计算机实验室）；École Polytechnique de Montréal（蒙特利尔综合理工学院）

This paper describes Artex, another algorithm for Automatic Text Summarization. In order to rank sentences, a simple inner product is calculated between each sentence, a document vector (text topic) and a lexical vector (vocabulary used by a sentence). Summaries are then generated by assembling the highest ranked sentences. No ruled-based linguistic post-processing is necessary in order to obtain summaries. Tests over several datasets (coming from Document Understanding Conferences (DUC), Text Analysis Conferences (TAC), evaluation campaigns, etc.) in French, English and Spanish have shown tha…

2012-10-11cs.AIcs.CLcs.IR