boosting factual correctness of abstractive summarization with knowledge graph

We propose a simple-to-use metric, matched relation tuples, to evaluate factual correctness in abstractive summarization. Boosting factual correctness of abstractive summarization with knowledge graph C Zhu, W Hinthorn, R Xu, Q Zeng, M Zeng, X Huang, M Jiang arXiv preprint arXiv:2003.08612 , 2020 al., 2019).I have tried to collect and curate some publications form Arxiv that related to the abstractive summarization, and the … Setting out to ensure diversity, we select a total of four di erent abstractive summarization systems by di erent authors, two of which leverage transfer learning. Chenguang Zhu (Microsoft Research) et al, On arXiv 2020. Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events. Office: 578 Allen Center. Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. Postdoctoral Researcher. Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work. Recently, the pre-trained BERT model achieves very successful results in many NLP classification / sequence labeling tasks. Schedule. Recent work has focused on building evaluation models to verify the factual correctness of semantically constrained text generation tasks such as document summarization. [4] Zhu, Chenguang, et al. Mini-Break. In this paper, we firstly propose a Fact-Aware Summarization model, FASum, which extracts factual relations from the article and integrates this knowledge into the decoding process via neural graph computation. 2021-05-25 BASS: Boosting Abstractive Summarization with Unified Semantic Graph Wenhao Wu, Wei Li, Xinyan Xiao, Jiachen Liu, Ziqiang Cao, Sujian Li, Hua Wu, Haifeng Wang arXiv_CL arXiv_CL Salient Pose Relation Attention Summarization PDF [2017] Presentation 2: Amplayo et al. Best Paper Awards and Closing. 论文作者： Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang We then design a factual corrector model FC to automatically correct factual errors from summaries generated by existing systems. While existing abstractive summarization models can generate summaries which highly overlap with references, they are not optimized to be factually correct. Chenguang Zhu (Microsoft Research) et al, On arXiv 2020. 2018. 2.1 Document Summarization Document summarization, as explained before, is shortening a text to the relevant points pertaining Kryściński et al. Knowledge graph-augmented abstractive summarization with semantic-driven cloze reward. 2020. A Meta Evaluation of Factuality in Summarization Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, Jianfeng Gao [pdf] Multi-Fact Correction in Abstractive Text Summarization. This inconsistency between summary … View Sz-Rung Shiang’s profile on LinkedIn, the world’s largest professional community. WENHAO WU et. Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training. Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang arXiv20 BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization Kai Wang, Xiaojun Quan, Rui Wang ACL19 [pdf] [code] Text generation models can generate factually inconsistent text containing distorted or fabricated facts about the source text. Abstractive document summarization is an unsolved task with a lot of ideas. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. A commonly observed problem with abstractive summarization is the distortion or fabrication of factual information in the article. 9 December 2020. ... Joint Pre-training of Knowledge Graph and Language Understanding. Automatic abstractive summaries are found to often distort or fabricate facts in the article. "Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph." Main Conference Day 1 (Tuesday, November 5, 2019) Opening Remarks. Abstractive Text Summarization is the task of generating a short and concise summary that captures the salient ideas of the source text. Rob van Zoest. Search by author and title is available on the accepted paper listing . Automatic Summarization of Open-Domain Podcast Episodes. (2020). Single-document text summarization is the task of automatically generating a shorter version of a document while retaining its most important information. "Faithful to the original: Fact aware neural abstractive summarization." arXiv preprint arXiv:1611.01462 (2016). We demonstrate this by augmenting the retrieval corpus of REALM, which includes only Wikipedia text. We will first give an overview of document summarization, then multi-document summarization and finally language models and embeddings. I will be joining the School of Computer and Communication Sciences at EPFL as an Assistant Professor in Fall 2021. The synthesis process of document content and its visualization play a basic role in the context of knowledge representation and retrieval. Discourse and Pragmatics, Summarization and Generation. Stanford University. Building a morpho-semantic knowledge graph for Arabic information retrieval; Deep Reinforcement Learning for Information Retrieval: Fundamentals and Advances; Co-search: Covid-19 information retrieval with semantic search, question answering, and abstractive summarization Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph. PDF | Video; Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph. Instead, we investigate several less-studied aspects of neural abstractive summarization, including (i) the importance of selecting important segments from transcripts to serve as input to the summarizer; (ii) striking a balance between the amount and quality of training instances; (iii) the appropriate summary … AAAI . Summarization is a cognitively challenging task – extracting summary worthy sentences is laborious, and expressing semantics in brief when doing abstractive summarization is complicated. ... 66 - Abstractive Summarization. Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph[J]. This page shows a preliminary version of the EMNLP-IJCNLP 2019 main conference schedule, with basic information on the dates of the talk and poster sessions. Text summarization using transfer learnin: Extractive and abstractive summarization using BERT and GPT-2 on news and podcast data V RISNE, A SIITOVA – 2019 – odr.chalmers.se A summary of a long text document enables people to easily grasp the information of the topic without having the need to read the whole document. x and Gare separately consumed by a document en-coder and a graph encoder, as presented in § 4.1. 19 Mar 2020 • Chenguang Zhu • William Hinthorn • Ruochen Xu • Qingkai Zeng • Michael Zeng • Xuedong Huang • Meng Jiang. Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D. Manning and Curtis Langlotz. … The Python machine learning library, Scikit-Learn, supports different implementations of gradient boosting classifiers, including XGBoost. Tying word vectors and word classifiers: A loss framework for language modeling. Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views: Zhizhong Han, Xinhai Liu, Yu-Shen Liu, Matthias Zwicker. [P12] Validating Label Consistency in NER Data Annotation by Q. Zeng, M. Yu, W. Yu, T. Jiang, T. Weninger, M. Jiang. Luyang Huang (Northeastern University) et al, In ACL 2020. Integrating Knowledge Graph and Natural Text for Language Model Pre-training Our evaluation shows that KG verbalization is an effective method of integrating KGs with natural language text. The authors of Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph have not publicly listed the code yet. We present a method to produce abstractive summaries of long documents that exceed several thousand words via neural abstractive summarization. Relation classification is an important NLP task to extract relations between entities. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. More details will be provided later. This inconsistency between summary and original text has seriously impacted its applicability. 论文标题： Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently been used to win many Kaggle data science competitions. 06/09/2021 ∙ by Weijia Shi, et al. Antoine Bosselut. Text summarization using transfer learnin: Extractive and abstractive summarization using BERT and GPT-2 on news and podcast data V RISNE, A SIITOVA – 2019 – odr.chalmers.se A summary of a long text document enables people to easily grasp the information of the topic without having the need to read the whole document. We propose a fact-aware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention. Dialog and Interactive Systems, Speech, Vision, Robotics, Multimodal and Grounding. C Zhu, W Hinthorn, R Xu, Q Zeng, M Zeng, X Huang, M Jiang. This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, Meng Jiang, Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph, In Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), 2021 . proposed a graph-based summarization framework (Opinosis) that creates succinct abstractive summaries of highly redundant opinions, it utilizes shallow NLP and expects no domain knowledge. Text Summarization, Knowledge Graph, Task-oriented Dialogues. Fine-Grained Event Trigger Detection Duong Le and Thien Huu Nguyen. CoRR abs/2010.00796 (2020) [i13] Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. (pytorch) [Summarization] Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph. 81 - Knowledge Graph Visualization. To the best of the authors’ knowledge, FASUM is the first approach to leverage knowledge graph in boosting factual correctness, while FC is the first summary-correction model for factual correctness. The fact that automatic summarization may produce plausible-sounding yet inaccurate summaries is a major concern that limits its wide application. This is a preliminary schedule and subject to change. Yin Jou Huang, Sadao Kurohashi. Analytics Vidhya - Learn Machine learning, artificial intelligence, business analytics, data science, big data, data visualizations tools and techniques. 2 Related Work 2.1 Abstractive Summarization [Summarization] Knowledge Graph-Augmented Abstractive Summarization with Semantic-Driven Cloze Reward. But what do these tasks entail? Session 12. Periodic Table of NLP Tasks - Russian chemist Dmitri Mendeleev published the first Periodic Table in 1869. Request code directly from the authors: Ask Authors for Code Get an expert to implement this paper: Request Implementation (OR if you have code to share with the community, please submit it here ️) Hacker Noon reflects the technology industry with unfettered stories and opinions written by real tech professionals. Seattle, WA 98195-2350. Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph. Abstractive Text Summarization is an important and practical task, aiming to rephrase the input text into a short version summary, while preserving its same and important semantics. Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training. The graph based approach for text summarization is an unsupervised technique,where we rank the required sentences or words based on a graph. In the graphical method the main focus is to obtain the more important sentences from a single document. Basically, we determine the importance of a vertex within a graph. (2019) employed an entity-aware transformer structure to boost the factual correctness, where the entities come from the Wikidata knowledge graph. 04/30/2021 ∙ by Yichong Huang, et al. DESCGEN: A Distantly Supervised Dataset for Generating Abstractive Entity Descriptions. See the virtual infrastructure blog post for more information about the formats of the presentations. Text generation models can generate factually inconsistent text containing distorted or fabricated facts about the source text. In this paper, we propose a Fact-Aware Summarization model, FASum, which extracts factual relations from the article to build a knowledge graph and integrates it into the neural decoding process. Apply this Fall if you are interested in working with me. 100 Best Automatic Summarization Videos | 100 Best GitHub: Automatic Summarization Abstract Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. We show that this extractive step … Email: qzeng [at] nd [dot] edu 2018. Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph - CORE Reader. relation tuples, to evaluate factual correctness in abstractive summarization. Their framework consists of four stages (Generation of a valid path, path scoring, collapsed paths, and finally the generation of the summary). The summarization task was the same for all systems and the same dataset was used, Biography. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey. [3] Cao, Ziqiang, et al. Session 11. [Summarization] Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph. Abstractive summarization systems generate new phrases that express a text by using as few words as possible. In this work, we propose SpanFact, a suite of two neural-based factual correctors that improve summary factual correctness without sacrificing informativeness. arXiv preprint arXiv:2003.08612. approaches to abstractive summarization, in contrast, are based on datasets whose target summaries are either a single sentence, or a bag of standalone sentences (e.g., extracted highlights of a story), neither of which allows for learning coherent narrative flow in the output summaries. Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. Highlight: In this paper, we present BASS, a novel framework for Boosting Abstractive Summarization based on a unified Semantic graph, which aggregates co-referent phrases distributing across a long range of context and conveys rich relations between phrases. summarization model to improve factual correctness. Then, we propose a Factual Corrector model, FC, that can modify abstractive summaries generated by any model to improve factual correctness. Now it's time for the NLP tasks to be organized in the Periodic Table style! Boosting factual correctness of abstractive summarization with knowledge graph. We believe we can get closer to the truth by elevating thousands of voices. In this paper, we specifically look at the problem of summarizing scientific research papers from multiple domains. 12: 2020: Meeting transcription using virtual microphone arrays. Nikola I. Nikolov, Richard H.R. arXiv preprint. Entity-level Factual Consistency of Abstractive Text Summarization. The state-of-the-art methods for relation classification are primarily based on Convolutional or Recurrent Neural Networks. To the reader, we pledge no paywall, no pop up ads, and evergreen (get it?) CoRR abs/2010.00796 (2020) [i11] Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, Bing Xiang. 9 Nov 2020. We perform a simple extractive step before generating a summary, which is then used to condition the transformer language model on relevant information before being tasked with generating a summary. Entity-level Factual Consistency of Abstractive Text Summarization Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown and Bing Xiang. Hakan Inan, Khashayar Khosravi, and Richard Socher. 2020. "Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization." US10909157B2 US16/051,188 US201816051188A US10909157B2 US 10909157 B2 US10909157 B2 US 10909157B2 US 201816051188 A US201816051188 A US … Then, we propose a Factual Corrector model, FC, that can modify abstractive summaries generated by any summarization model to improve factual correctness. ... Joint Pre-training of Knowledge Graph and Language Understanding. Information Retrieval and Document Analysis, Lexical Semantics, Sentence-level Semantics, Machine Learning. A commonly observed problem with the stateof-the art abstractive summarization models is that the generated summaries can be factually inconsistent with the input documents. We are not allowed to display external PDFs yet. We then design a factual corrector model FC […] This inconsistency between summary and original text has seriously impacted its applicability. Our model takes as input a document, represented as a sequence of tokens x = fx kg, and a knowledge graph Gconsisting of nodes fv ig. Abstractive summarization might fail to preserve the meaning of the original text and generalizes less than extractive summarization. Danqing Wang (Fudan University) et al, In ACL 2020. 结论： FASUM can generate summaries with higher factual correctness compared with state-of-the-art abstractive summarization systems. Experienced research manager in deep learning and its applications in NLP, e.g. Connected Papers is a visual tool to help researchers and applied scientists find academic papers relevant to their field of work. Existing methods for tag-clouds generations are mostly based on text content of documents, others also consider statistical or semantic information to enrich the document summary, while precious information deriving from multimedia content is often … Highlight: Inspired by recent work on evaluating factual consistency in abstractive summarization (Durmus et al., 2020; Wang et al., 2020), we propose an automatic evaluation metric for factual consistency in knowledge-grounded dialogue models using automatic question generation and … FC improves the factual correctness of summaries generated by various models via only modifying several entity tokens. In this work, we design a graph encoder based on conversational structure, which uses the sparse relational graph self-attention network to obtain the global features of dialogues. University of Washington. Still, you can think about building NLP Pipelines out of standard NLP tasks. Automatic abstractive summaries are found to often distort or fabricate facts in the article. Times are displayed in your local timezone. arXiv preprint arXiv:2005.01159 (2020). Abstractive Text Summarization. Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization. Sz-Rung has 5 jobs listed on their profile. Recent work has focused on building evaluation models to verify the factual correctness of semantically constrained text generation tasks such as document summarization. arXiv preprint arXiv:2101.08698, January 2021. [2018] Paper 2: Fan et al. arXiv preprint arXiv:2003.08612. However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge. 22.01 - Factual Correctness, Background knowledge (PS) Presentation 1: Cao et al. Fax: 206-685-2969. email: yejin@cs.washington.edu. Extremely Small BERT Models from Mixed-Vocabulary Training knowledge graphs or text entailment signals. To the best of the authors’ knowledge, FASum is the first approach to leverage knowledge graph in boosting factual correctness, while FC is the first summary-correction model for factual correctness. stractive summarization systems produce and how they a ect the factual correctness of summaries. Research about Abstractive Summarization Published in ArXiv 4 minute read Abstractive summary is a technique in which the summary is created by either rephrasing or using the new words, rather than simply extracting the relevant phrases (Gupta et. [2018] Presentation 2: Fabbri et al. [2019] For HS: Paper 1: Liu* et al. 文本摘要是NLP中非常重要的一项任务，即给定一篇长文章，模型生成一小段文本作为对该文章的摘要。总的来讲，文本摘要分为抽取式与抽象式。前者是直接从文章中选取片段作为摘要，后者是从头开始生成一段文本作为摘要。 显然，抽取式文本摘要的好处是它能保留文章的原始信息，但缺点是它只能从原文章中选取，相对不那么灵活。而抽象式摘要尽管能更加灵活地生成文本，但是它经常包含很多错误的“事实性知识”——错误地生成了原文章本来的信息。 比如，原文章包含了一个重要事实（观点）：“诺兰于201… [5] Gunel, Beliz, et al. 【7】Zhu C, Hinthorn W, Xu R, et al. [2018] 29.01 - Abstractive Multi-Document-Summarization (PS/HS) Presentation 1: Lebano et al. Question Answering, Textual Inference and Other Areas of Semantics. Paul G. Allen School of Computer Science & Engineering. Fusing Context Into Knowledge Graph for Commonsense Question Answering. et al. [Summarization] Heterogeneous Graph Neural Networks for Extractive Document Summarization. Boosting factual correctness of abstractive summarization with knowledge graph. Boosting factual correctness of abstractive summarization with knowledge graph. Abstract Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. It is very … Box 352350. Contact. arXiv preprint arXiv:2003.08612, 2020. Allen Institute for Artificial Intelligence. Abstractive methodologies summarize texts differently, using deep neural networks to interpret, examine, and generate new content (summary), including essential concepts from the source. content. The study revealed that in the current setting the training signal is dominated by biases present in summarization datasets preventing models from learning accurate content selection.