On Generating Extended Summaries of Long Documents | Research Paper Walkthrough

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Published at : 07 Jan 2021
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#textsummarisation #bert #naturallanguageprocessing
This video talks about very recent work in the domain of text summarization. Researchers introduce a method for generating extended extractive summaries of Long documents such as research papers, legal document using Multi-task loss. The work surpasses BERTSUM on ROUGE scale.

P.S. Sorry for bad audio at certain segments in the video.

⏩ Abstract: 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.

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⏩ OUTLINE:
0:00 - Abstract
3:42 - Dataset (Longsumm, arXiv-long, PubMed-long)
4:54 - BERTSUM Paper Explained (Extractive Summarization Method)
10:00 - Section Aware Summarizer
12:26 - Analysis and Results

⏩ Paper Title: On Generating Extended Summaries of Long Documents
⏩ Paper: https://arxiv.org/pdf/2012.14136.pdf
⏩ Paper Code: https://github.com/Georgetown-IR-Lab/ExtendedSumm
⏩ Author: Sajad Sotudeh, Arman Cohan, Nazli Goharian
⏩ Organisation: IR Lab, Georgetown University, Allen Institute for Artificial,

⏩ IMPORTANT LINKS
BERTSUM Paper - https://arxiv.org/abs/1903.10318
BERT for Summarizing Lectures - https://www.youtube.com/watch?v=JU6eSLsp6vI&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f&index=2
COVID-19 Article Summarization - https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f&index=1
Extractive and Abstractive Summarization using Transformer Language Models - https://www.youtube.com/watch?v=2IzXW3Ypks0&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f&index=4
PEGASUS: Abstractive Summarization - https://www.youtube.com/watch?v=QY8oZxS0txs&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f&index=7

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