fasum - Automatic abstractive summaries are found to oxdera often distort or fabricate facts in the article This inconsistency between summary and original text has seriously impacted its applicability We propose a factaware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention We then design a factual corrector model FC Results show that on CNNDailyMail FASUM obtains 06 higher fact consistency scores than UNILM Dong et al 2019 and 39 higher than BOTTOMUP Gehrmann et al 2018 More over after correction by FC the factual score of summaries from BOTTOMUP increases 14 on CNNDailyMail and 09 on XSum and the score of summaries from TCONVS2S Mar 19 2020 This inconsistency between summary and original text has led to various concerns over its applicability In this paper we firstly propose a FactAware Summarization model FASum which extracts factual relations from the article and integrates this knowledge into the decoding process via neural graph computation Jun 11 2021 A factaware summarization model FASum is proposed to extract and integrate factual relations into the summary generation process via graph attention and a factual corrector model FC is designed to automatically correct factual errors from summaries generated by existing systems Automatic abstractive summaries are found to often distort or fabricate facts in the article This inconsistency Mar 19 2020 Empirical results show that FASum generates summaries with significantly higher factual correctness compared with stateoftheart abstractive summarization systems both under an independently trained factual correctness evaluator and human evaluation For example in CNNDailyMail dataset FASum obtains 12 higher than BottomUp READ FULL TEXT Jan 3 2025 FASum is a model that extracts and integrates factual relations into abstractive summarization to enhance factual consistency It is presented in a paper published in the 2021 Conference of the North American Chapter of the Association for Computational Linguistics Sep 24 2022 FASum enhances the factuality of the generated summary by encoding the triples extracted from the source document through the graph neural network FC is a fact corrector introduced in FASum which aims at correcting the generated summary through the replacement of nouns FASumFC is the result of FASum corrected the factual errors by FC Apr 1 2017 Request PDF On Apr 1 2017 M Thahaseen Fathima and others published FASUM Feature Accelerated SingleVideo Summarization Find read and cite all the research you need on ResearchGate Mar 19 2020 This inconsistency between summary and original text has led to various concerns over its applicability In this paper we firstly propose a FactAware Summarization model FASum which extracts factual relations from the article and integrates this knowledge into the decoding process via neural graph computation fasum Urban Dictionary Boosting Factual Correctness of Abstractive Summarization Mar 19 2020 Empirical results show that FASum generates summaries with significantly higher factual correctness compared with stateoftheart abstractive summarization systems both under an independently Mar 19 2020 Automatic abstractive summaries are found to often distort or fabricate facts in the article This inconsistency between summary and original text has seriously impacted its pandan138 applicability We propose a factaware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention We then design a factual corrector model FC to arXiv200308612v8 csCL 15 Mar 2021 200308612v4 Boosting Factual Correctness of Abstractive Boosting Factual Correctness of Abstractive Summarization Employing Internal and External Knowledge to Factuality Jun 1 2012 a short word for for some reason 19992023 Urban Dictionary Enhancing Factual Consistency of Abstractive Summarization Chenguang Zhu 1 William Hinthorn Ruochen Xu Qingkai Zeng2 Michael Zeng 1 Xuedong Huang Meng Jiang2 1 Microsoft Cognitive Services Group This includes the produced summary from all the systems UniLM BottomUp TConvS2S FASum and the corresponding corrected summaries by the model FC on the test set of CNNDailyMail and XSUM It also contains the reference summary and the corresponding articles in the test set If you use our prediction results please cite our paper Sep 24 2022 Based on FASum Zhu et al propose a corrector FC to modify errors in summary represented by FASumFC here Bart 8 is a bidirectional and autoregressive pretrained model with denoising strategy we use the finetuned model Bartlargecnn Bartlargexsum from Hugging Face for CNNDM XSUM test set Enhancing Factual Consistency of Abstractive Summarization Crossmodal knowledge guided model for abstractive Springer Mar 19 2020 A FactAware Summarization model FASum is proposed which extracts factual relations from the article to build a knowledge graph and integrates it into the neural decoding process and improves the factual correctness of summaries generated by various models via only modifying several entity tokens A commonly observed problem with abstractive summarization is the distortion or fabrication of FASUM Feature Accelerated SingleVideo Summarization Enhancing Factual Consistency of Abstractive Summarization FASUM Feature Accelerated SingleVideo Summarization Abstract Plenty of video stuff is created broadcasted shared and stored each and every day by industry experts beginners and hobbyists Video summaries aim at showcasing the semantics and content of a clip in reduced time and space to enable a quick overview of video clip relevance Adversarial FineGrained Fact Graph for FactualityOriented Boosting Factual Correctness of Abstractive Summarization GitHub zcgzcgzcg1FASum Prediction results for our NAACL Enhancing Factual Consistency of Abstractive Summarization Jul 27 2023 Zhu et al proposed a factaware summary model FASUM and a factcorrector model FC which frames the correction process as a seq2seq problem to make the corrected summary more realistically consistent with the article These models are indeed effective in improving the factual consistency of AS but often at the cost of greatly reducing the FASUM Feature Accelerated SingleVideo Summarization 200308612 Enhancing Factual Consistency of Abstractive Enhancing Factual Consistency of Abstractive Summarization Automatic abstractive summaries are found to often distort or fabricate facts in the article This inconsistency between summary and original text has seriously impacted its applicability We propose a factaware summarization model FASum to extract and integrate factual relations into the summary generation process via graph attention We then design a factual corrector model FC to Enhancing Factual Consistency of Abstractive Summarization Boosting Factual pasangan123 Correctness of Abstractive Summarization
nyekel
klaker
