Dialogue, the most fundamental and specially privileged arena of language, gains increasing ubiquity across the Web in recent years. Quickly going through the long dialogue context and capturing salient information scattered over the whole dialogue session benefit users in many real-world Web applications such as email thread summarization and meeting minutes draft. Dialogue summarization is a challenging task in that dialogue has dynamic interaction nature and presumably inconsistent information flow among various speakers. Many researchers address this task by modeling dialogue with pre-computed static graph structure using external linguistic toolkits. However, such methods heavily depend on the reliability of external tools and the static graph construction is disjoint with the graph representation learning phase, which makes the graph can't be dynamically adapted for the downstream summarization task. In this paper, we propose a Static-Dynamic graph-based Dialogue Summarization model (SDDS), which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion. To verify the effectiveness of SDDS, we conduct experiments on three benchmark datasets (SAMSum, MediaSum, and DialogSum) and the results verify the superiority of SDDS.

<iframe src="https://app.sli.do/event/oApzFnCrRo86e5qieE2QHQ/embed/polls/759591c5-6c68-4ee0-8db5-09d5d405ab9d" width="300" height="400"></iframe>

Dialogue Summarization with Static-Dynamic Structure Fusion Graph

A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input.In this work, we first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise.To create semantic-consistent substitutes, we propose a SummAttacker, which is an efficient approach to generating adversarial samples based on pre-trained language models.Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets.Next, we analyze the vulnerability of the summarization systems and explore improving the robustness by data augmentation.Specifically, the first vulnerability factor we found is the low diversity of the training inputs.Correspondingly, we expose the encoder to more diverse cases created by SummAttacker in the input space.The second factor is the vulnerability of the decoder, and we propose an augmentation in the latent space of the decoder to improve its robustness.Concretely, we create virtual cases by manifold softmixing two decoder hidden states of similar semantic meanings.Experimental results on Gigaword and CNN/DM datasets demonstrate that our approach achieves significant improvements over strong baselines and exhibits higher robustness on noisy, attacked, and clean datasets

<iframe src="https://app.sli.do/event/eX4PwSmcCc1EvCqjxcYdKB/embed/polls/a187e813-d51e-44e6-a4f7-d57eba1f9415" width="300" height="400"></iframe>

Improving the Robustness of Summarization Systems with Dual Augmentation

In a citation graph, adjacent paper nodes share related scientific terms and topics. 
The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper summarization task, for exploring beyond the intra-document information.
In this work, we focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings.
We first propose a Multi-granularity Unsupervised Summarization model (MUS) as a simple and low-cost solution to the task.
MUS finetunes a pre-trained encoder model on the citation graph by link prediction tasks.
Then, the abstract sentences are extracted from the corresponding paper considering multi-granularity information.
Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework.
Motivated by this, we next propose a Graph-based Supervised Summarizationmodel (GSS) to achieve more accurate results on the task when large-scale labeled data are available.
Apart from employing the link prediction as an auxiliary task, GSS introduces a gated sentence encoder and a graph information fusion module to take advantage of the graph information to polish the sentence representation.
Experiments on a public benchmark dataset show that MUS and GSS bring substantial improvements over the prior state-of-the-art model.

Scientific Paper Extractive Summarization Enhanced by Citation Graphs

Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases.
However, debiasing on the Chinese language, one of the most spoken languages, has been less explored.
Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming.
In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data.
Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, \ie a kind of component in Chinese characters, during the training procedure.
This consequently alleviates discriminative gender biases.
Experimental results show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.

Unsupervised Mitigation of Gender Bias by Character Components: A Case Study of Chinese Word Embedding

Virtual Poster Session 2

poster

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

Poster Session 1

**Topics:** Multilinguality 
NLP Applications 
Natural Language Generation 
Question Answering 
Resources and Evaluation 
Semantics: Lexical, Sentence level, Textual Inference and Other areas 
Speech, Vision, Robotics, Multimodal Grounding 
Summarization 
Theme Track 
Unsupervised and Weakly-Supervised Methods in NLP

Poster Session 14

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well: we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

 *Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA* 
***EMNLP 2022 General Chair***
 
[![](https://assets.underline.io/uploads/markdown_image/1/image/9eec7d4a287ee18c278b08229290aa83.png)](https://drive.google.com/file/d/1OlPv6QBeo62VVTughj2jkiLeyHd1WnUt/view)
 
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Register here

EMNLP 2022

Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

**[Please visit our website.](https://genderbiasnlp.talp.cat/)**

W23: Workshop on Gender Bias in Natural Language Processing

workshop paper

## Welcome to NAACL 2022!
The Annual Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL) is taking place July 10-15, 2022 as a hybrid event, in Seattle, WA and online. We are happy to welcome all of you to conference! 
 
The main conference program features oral presentations, in-person and virtual posters and demo sessions, a plenary session for our best paper presentations and awards, keynote presentations, a plenary panel on the place of linguistics and symbolic structures in NLP, and an Industrial Track panel on Careers in NLP.
 
Posters (including Findings of NAACL 2022) and demos are grouped by areas for both the in-person and the virtual sessions. For the virtual component, the talks will be on Zoom and the posters and the demos will be in GatherTown. The Student Research Workshop and will have an oral session and a poster session.
 
The program also features 6 Tutorials and 26 Workshops.
 
 We wish you a wonderful conference!
 
[The NAACL 2022 Organizing Committee](https://2022.naacl.org/committees/organization/)

NAACL 2022

2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics

Xiuying Chen

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SHORT BIO

Presentations

Dialogue Summarization with Static-Dynamic Structure Fusion Graph

Improving the Robustness of Summarization Systems with Dual Augmentation

Scientific Paper Extractive Summarization Enhanced by Citation Graphs

Unsupervised Mitigation of Gender Bias by Character Components: A Case Study of Chinese Word Embedding

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