TALKSUMM: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
This paper presents a new way to summarize scientific papers using videos of conference talks. The authors collected a large dataset and developed a method that automatically generates summaries, which perform well compared to traditional methods.
This video presentation explains the key concepts from the paper in plain language.
Content & Liability Disclaimer
This article and its accompanying video are automated summaries derived from the original research paper by Unknown authors. The original research was conducted solely by the paper's authors; PDFdigest did not conduct any of the research and makes no claims of ownership over the underlying scientific work.
The video narration is generated by artificial intelligence and references the paper's authors for attribution. The video is not narrated by any of the paper's authors. This content may contain inaccuracies, omissions, or misinterpretations of the original research. First-person language (e.g., "we found", "our results") reflects the original authors' voice, not PDFdigest's. Always read the original paper for accurate, verified information before making any decisions based on this content.
This content is provided "as is" without any warranties, express or implied. Simulated systems OÜ, its officers, directors, employees, and agents shall not be liable for any direct, indirect, incidental, special, consequential, or punitive damages arising from your use of, reliance on, or access to this content, including but not limited to errors, omissions, or misinterpretations of the original research. This disclaimer applies to the fullest extent permitted by applicable law.
- 1 We aim to retrieve those source sentences and use them as the summary given the transcript.
- 2 Several suggested methods to reduce summarization efforts are not scalable because they require human annotations.
- 3 We directly compare their reported model performance to ours, including their ABSTRACT baseline, as we use the same test set as in Yasunaga et al.
- 4 The different colors show corresponding content between the transcript and the written paper.
Introduction
The increasing rate of scientific publications makes it almost impossible for researchers to keep up with relevant research. Citations-based and content-based approaches are the two common methods for summarizing scientific papers.
The lack of large-scale training data makes scientific paper summarization less studied than news domain summarization.
The length and complexity of papers require substantial summarization effort from experts.
The transcript cannot serve as a good summary for the corresponding paper because it constitutes only one modality of the talk and cannot stand by itself as coherent written text.
Research Question
We aim to retrieve those source sentences and use them as the summary given the transcript.
Methodology
Narayan et al. introduced a dataset, evaluation method, and baseline systems for the Split-and Rephrase task. We propose a more challenging split of the data to aid further research on the task.
Study Design
We encourage future work on the split-and-rephrase task to use our new data split or the v1.0 split instead of the original one.
Processing long, complex sentences is a hard task for humans and NLP systems, so we ask if we can automatically break a complex sentence into several simple ones while preserving the meaning.
We propose a more challenging split of the data to aid further research on the task.
We encourage future work on the split-and-rephrase task to use our new data split or the v1.0 split instead of the original one.
How PDFdigest Helps You Understand Research
Instant Paper Analysis
Get structured summaries and key findings from dense PDFs in seconds.
Visual Explanations
Turn complex methods, figures, and results into clearer visual breakdowns.
AI-Powered Q&A
Ask focused questions and get answers grounded in the paper.
Results & Findings
Several suggested methods to reduce summarization efforts are not scalable because they require human annotations. Feeding the model with examples containing entities alone without any facts about them causes it to output perfectly phrased but unsupported facts.
- Several suggested methods to reduce summarization efforts are not scalable because they require human annotations.
- Feeding the model with examples containing entities alone without any facts about them causes it to output perfectly phrased but unsupported facts.
- We establish a stronger baseline by extending the SEQ2SEQ approach with a copy mechanism.
- The different colors show corresponding content between the transcript and the written paper.
- We map the transcripts to the corresponding papers’ text using unsupervised alignment algorithms to create extractive summaries.
Several suggested methods to reduce summarization efforts are not scalable because they require human annotations.
We directly compare their reported model performance to ours, including their ABSTRACT baseline, as we use the same test set as in Yasunaga et al.
Practical Applications
Automatic text summarization could help mitigate this problem. Our dataset can easily grow in size as more conference videos are aggregated, although our summaries may be noisy.
Data Collection
The authors collected 1716 video talks from various NLP and ML conferences, extracting transcripts and corresponding papers. The coherence of the talks makes them suitable for summarization.
Figures Explained
Frequently Asked Questions
This is the first approach to automatically create extractive summaries for scientific papers by utilizing the videos of conference talks. We aim to retrieve those source sentences and use them as the summary given the transcript.
We show that simple neural models perform well on the original benchmark due to memorization, propose a more challenging data split to discourage this, and perform evaluation showing the task is far from being solved.
Several suggested methods to reduce summarization efforts are not scalable because they require human annotations. We directly compare their reported model performance to ours, including their ABSTRACT baseline, as we use the same test set as in Yasunaga et al.
Automatic text summarization could help mitigate this problem. Our dataset can easily grow in size as more conference videos are aggregated, although our summaries may be noisy.
We propose a more challenging split of the data to aid further research on the task. We encourage future work on the split-and-rephrase task to use our new data split or the v1.0 split instead of the original one.
This paper presents a new way to summarize scientific papers using videos of conference talks. The authors collected a large dataset and developed a method that automatically generates summaries, which perform well compared to traditional methods.