Dominican Republic

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this paper, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task.
We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries).

EMNLP 2021

Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

<iframe style="width:700px;height:400px" src="https://online.fliphtml5.com/ebtyf/ceby/"  seamless="seamless" scrolling="no" frameborder="0" allowtransparency="true" allowfullscreen="true" ></iframe>

Please Note: The EMNLP registration system is not currently connected to the underline site as we are still in the process of building out EMNLP 2021. You will receive access instructions from underline the week of November 1st. 

Access is given only to EMNLP upon registration, if you have not registered please do so [here](https://2021.emnlp.org/registration).

Registered attendees will receive access the week of November 1st.

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

technical paper

{C}onvex {A}ggregation for {O}pinion {S}ummarization

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

Multilingual Chart-based Constituency Parse Extraction from Pre-trained Language Models

APGN: Adversarial and Parameter Generation Networks for Multi-Source Cross-Domain Dependency Parsing

Comparing learnability of two dependency schemes: ‘semantic’ (UD) and ‘syntactic’ (SUD)

A Corpus-based Syntactic Analysis of Two-termed Unlike Coordination

Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling

Is BERT a Cross-Disciplinary Knowledge Learner? A Surprising Finding of Pre-trained Models’ Transferability

NewsBERT: Distilling Pre-trained Language Model for Intelligent News Application

SPRING Goes Online: End-to-End AMR Parsing and Generation

poster

workshop paper

https://d3smihljt9218e.cloudfront.net/transcript/24970/subtitle/d3b735654e418a42869f0bd43deb03c8.vtt

English (automatic)

Leveraging Pretrained Models for Automatic Summarization of Doctor-Patient Conversations

Downloads

Next from EMNLP 2021

{C}onvex {A}ggregation for {O}pinion {S}ummarization

Similar lecture

MiRANews: Dataset and Benchmarks for Multi-Resource-Assisted News Summarization

VIDEO LIBRARY

EVENTS

COMPANY

RESOURCES