text: 392,16,1344346476617592833,825458566689648645,Sajad Sotudeh,"I'm excited to announce our new paper: ""On Generating Extended Summaries of Long Documents"" w/ @armancohan and Nazli Goharian.  arXiv: <LINK> Code and Datasets: <LINK> (1/4) <LINK> We propose a novel multi-tasking approach that exploits the hierarchical structure of long scientific documents to aid the extractive summarization model in selecting summary-worthy sentences, and finally, form the ""extended"" summaries of the given long documents. (2/4) In order to support this task, we additionally collect two extended summarization datasets: arXiv-Long, and PubMed-Long. The experimental results indicate that the multi-tasking model either outperforms or matches the performance of the prior baseline (i.e., BertSumExt). (3/4) The extrinsic analysis: 1) our model improves consistently as the summary length increases; 2) our model adjusts the extraction probability of sentences toward salient sentences across diverse sections of the source document and pick those with higher confidence. (4/4) <LINK>",https://arxiv.org/abs/2012.14136,"Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it is desirable in some cases to know more detailed information about its salient points that can't fit in a short summary. This is typically the case for longer documents such as a research paper, legal document, or a book. In this paper, we present a new method for generating extended summaries of long papers. Our method exploits hierarchical structure of the documents and incorporates it into an extractive summarization model through a multi-task learning approach. We then present our results on three long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method outperforms or matches the performance of strong baselines. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for long-form summary generation task. Our analysis shows that our multi-tasking approach can adjust extraction probability distribution to the favor of summary-worthy sentences across diverse sections. Our datasets, and codes are publicly available at this https URL ",On Generating Extended Summaries of Long Documents,4,"['I\'m excited to announce our new paper: ""On Generating Extended Summaries of Long Documents"" w/ @armancohan and Nazli Goharian. \n\narXiv: <LINK>\nCode and Datasets: <LINK>\n(1/4) <LINK>', 'We propose a novel multi-tasking approach that exploits the hierarchical structure of long scientific documents to aid the extractive summarization model in selecting summary-worthy sentences, and finally, form the ""extended"" summaries of the given long documents. (2/4)', 'In order to support this task, we additionally collect two extended summarization datasets: arXiv-Long, and PubMed-Long. The experimental results indicate that the multi-tasking model either outperforms or matches the performance of the prior baseline (i.e., BertSumExt). (3/4)', 'The extrinsic analysis: 1) our model improves consistently as the summary length increases; 2) our model adjusts the extraction probability of sentences toward salient sentences across diverse sections of the source document and pick those with higher confidence. (4/4) https://t.co/otpKom9Lov']",20,12,1006