Korea, Republic of

In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated. This phenomenon has been significantly amplified by the fast and inexorable spread of misinformation on social media and instant messaging platforms. With this work, we contribute to containing the negative impact on society caused by fake news. We propose a graph neural network approach for detecting false information on Twitter. We leverage the inherent structure of graph-based social media data aggregating information from short text messages (tweets), user profiles and social interactions. We use knowledge from pre-trained language models efficiently, and show that user-defined descriptions of profiles provide useful information for improved prediction performance. The empirical results indicate that our proposed framework significantly outperforms text- and user-based methods on misinformation datasets from two different domains, even in a difficult multilingual setting.

COLING 2022

True or False? Detecting False Information on Social Media Using Graph Neural Networks

fake news detection

graph neural networks

social media

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

COLING 2020

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

workshop paper

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

In fall of 2022, COLING will be held in Gyeongju in a hybrid format. All participants can either present at the venue site or join virtually. As more people get vaccinated, we are happy to provide safer environments for our colleagues. We believe that COLING 2022 will be one of the conferences, free from the pandemic. The hybrid format gives presenters and sponsors a valuable opportunity to promote their companies in both an online and in-person venue. For the first time in a long time, customers can interact with their sponsor's products first-hand. The online venue, too, gives sponsors the chance to network with those unable to attend the in-person session.

The hybrid format ultimately lends itself to greater exposure for our sponsors: COLING2022 will let you reach more potential partners and customers than ever before!<br><br><br>

To gain access to this event page you are required to register.<br> Find more information and pay the registration fee on event the organizer’s website **[https://coling2022.org/reg](https://coling2022.org/reg)**

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Scientific medical terms are difficult to understand for laypeople due to their technical formulas and etymology. Understanding medical concepts is important for laypeople as personal and public health is a lifelong concern. In this study, we present our methodology for building a French lexical resource annotated with paraphrases for the simplification of monolexical and multiword medical terms. In order to find medical paraphrases, we automatically searched for medical terms and specific lexical markers that help to paraphrase them. We annotated the medical terms, the paraphrase markers, and the paraphrase. We analysed the lexical relations and semantico-pragmatic functions that exists between the term and its paraphrase. We computed statistics for the medical paraphrase corpus, and we evaluated the readability of the medical paraphrases for a non-specialist coder. Our results show that medical paraphrases from popularization texts are easier to understand (62.66%) than paraphrases extracted from scientific texts (50%).

Identifying Medical Paraphrases in Scientific versus Popularization Texts in French for Laypeople Understanding

Summarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. We also report and discuss results from experiments applying common baseline extractive text summarization algorithms to this dataset. Qualitative analysis of the results reveal shortcomings in common annotation approaches and evaluation methods when applied to narrative and interactive narrative datasets. The dataset is released as open source for future researchers to train and test their own approaches for IDN text.

IDN-Sum : A New Dataset for Interactive Digital Narrative Extractive Text Summarisation

The present paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or
even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multi-layer network is constructed by establishing linkages between the different parts. During training phases, several hyper-parameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest.


Summarization of Long Input Texts Using Multi-Layer Neural Network

We present the results of the Workshop on Automatic Summarization for Creative Writing 2022 Shared Task on summarization of chapters from novels. In this task, we finetune a pretrained transformer model for long documents called LongformerEncoderDecoder which supports seq2seq tasks for long inputs which can be up to 16k tokens in length. We use the Booksum dataset for longform narrative summarization for training and validation, which maps chapters from novels, plays and stories to highly abstractive human written summaries. We use a summary of summaries approach to generate the final summaries for the blind test set, in which we recursively divide the text into paragraphs, summarize them, concatenate all resultant summaries and repeat this process until either a specified summary length is reached or there is no significant change in summary length in consecutive iterations. Our best model achieves a ROUGE-1 F-1 score of 29.75, a ROUGE-2 F-1 score of 7.89 and a BERT F- 1 score of 54.10 on the shared task blind test dataset.

LED Finteuning and Recursive Summary Generation for Automatic Summarization of Chapters from Novels

Challenges and solutions for summarization of book chapters and dialogues

Where did I read that? Challenges in Summarization of Book Chapters and Dialogues

The Creative Summarization Shared Task at COLING 2022 aspires to generate summaries given long-form texts from creative writing. This paper presents the system architecture and the results of our participation in the Scriptbase track that focuses on generating movie plots given movie scripts. The core innovation in our model employs a two-stage hierarchical architecture for movie script summarization. In the first stage, a heuristic extraction method is applied to extract actions and essential dialogues, which reduces the average length of input movie scripts by 66\% from about 24K to 8K tokens. In the second stage, a state-of-the-art encoder-decoder model, Longformer-Encoder-Decoder (LED), is trained with effective fine-tuning methods, \emph{BitFit} and \emph{NoisyTune}. Evaluations on the unseen test set indicate that our system outperforms both zero-shot LED baselines as well as other participants on various automatic metrics and ranks 1st in the Scriptbase track.

Two-Stage Movie Scripts Summarization: An Efficient Method For Low-resource Long Documents Summarization

This presentation is addressing the problem of prime-time television screenplay summarization based on the SummScreen Forever Dreaming dataset. Our approach utilizes extended Longformers combined with sketch supervision including categories specifically for scene descriptions. Our system was able to produce the shortest summaries out of all submissions. While some problems with factual consistency still remain, the system was scoring highest among competitors in the ROUGE and BERTScore evaluation categories.

Long Input Dialogue Summarization with Sketch Supervision for Summarization of Primetime Television Transcripts

This presentation will discuss our shared task paper, which introduces a generalized AMR scheme to better model the long conversations in TV episodes. We hope to expose the diverse relations between concepts and complex dialogue structures so that they will be more accessible to transformer encoders. We will also discuss how the high-level information in a scene of a TV episode may guide the encoding of local tokens and utterances.

AMR-augmented Hierarchical Network for TV TranscriptSummarization

How to efficiently turn long documents into summaries with explorable structure? This has become an increasingly compelling question, given the rapid growth of document amount, length, and complexity. Existing summarization systems face new challenges for long documents: (1) Most systems only produce a generic paragraph-level summary, and cannot include all important points in a long document. (2) State-of-the-art summarizers rely on Transformer, whose time complexity is quadratic to the document length, and scales to hundreds of words at best.

In this talk, I first discuss the design of efficient attentions for long document summarization to handle more than 10k tokens while prior work only processes hundreds of words. I then present a new task: hierarchical question-summary generation, where we summarize the salient content in the source document into a hierarchy of questions and summaries, where each follow-up question inquires about the content of its parent question-summary pair. Learnable hierarchical biases are designed as part of the Transformer attention calculation, to adjust attention weights based on tokens’ relative positions with regard to the document structure. For both work, I will introduce new datasets to support experiments and discuss evaluation challenges.

Long Document Summarization using Efficient Attentions and Document Structure

Figurative language uses words in a way that deviates from their conventionally accepted definitions in order to convey a more complicated meaning or heightened effect. Figurative language generation can be very useful in many downstream tasks including fiction writing, essay writing, dialogue systems, etc. In this talk, I will first give an overview of figurative language generation and then introduce our recent work on a special case of figurative language generation, that is, hyperbole generation. We construct the first large-scale hyperbole corpus HYPO-XL and propose MOVER, an unsupervised approach to hyperbole generation.

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