Singapore

EMNLP 2023

Enhancing Abstractiveness of Summarization Models through Calibrated Distillation

calibration

distillation

summarization

workshop paper

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

The conference is held in person at the Resorts World Convention Centre in Singapore, and available on-line with the help of Underline.

For those in Singapore, I hope that you will find time to explore the exciting Sentosa Island, Gardens by the Bay, Singapore Botanic Gardens and the many other attractions unique to Singapore.

Of course, we are grateful to our sponsors and partners for their generous support of EMNLP 2023, whose contributions make it possible for us to host this world-class event.

Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

To access the **EMNLP 2023** event page on Underline, you need to register for the Conference. 
Please follow **[this link](https://2023.emnlp.org/registration/)** for more details.

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Factual Relation Discrimination for Factuality-oriented Abstractive Summarization | VIDEO

Inverse Reinforcement Learning for Text Summarization

Hierarchical Catalogue Generation for Literature Review: A Benchmark

Text simplification has emerged as an increasingly useful application of AI for bridging the communication gap in specialized fields such as medicine, where the lexicon is often dominated by technical jargon and complex constructs. Despite notable progress, methods in medical simplification sometimes result in the generated text having lower quality and diversity. In this work, we explore ways to further improve the readability of text simplification in the medical domain. We propose (1) a new unlikelihood loss that encourages generation of simpler terms and (2) a reranked beam search decoding method that optimizes for simplicity, which achieve better performance on readability metrics on three datasets. This study's findings offer promising avenues for improving text simplification in the medical field.

Medical Text Simplification: Optimizing for Readability with Unlikelihood Training and Reranked Beam Search Decoding

A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization

Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization

Topic-Informed Dialogue Summarization using Topic Distribution and Prompt-based Modeling

DocAsRef: A Pilot Empirical Study on Repurposing Reference-based Summary Quality Metrics as Reference-free Metrics | VIDEO

Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

Re-Examining Summarization Evaluation across Multiple Quality Criteria | VIDEO

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