Michal Shmueli-Scheuer


2020

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Proceedings of the First Workshop on Scholarly Document Processing
Muthu Kumar Chandrasekaran | Anita de Waard | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Eduard Hovy | Petr Knoth | David Konopnicki | Philipp Mayr | Robert M. Patton | Michal Shmueli-Scheuer
Proceedings of the First Workshop on Scholarly Document Processing

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Overview of the First Workshop on Scholarly Document Processing (SDP)
Muthu Kumar Chandrasekaran | Guy Feigenblat | Dayne Freitag | Tirthankar Ghosal | Eduard Hovy | Philipp Mayr | Michal Shmueli-Scheuer | Anita de Waard
Proceedings of the First Workshop on Scholarly Document Processing

Next to keeping up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. To address these challenges, computational work on enhancing search, summarization, and analysis of scholarly documents has flourished. However, the various strands of research on scholarly document processing remain fragmented. To reach to the broader NLP and AI/ML community, pool distributed efforts and enable shared access to published research, we held the 1st Workshop on Scholarly Document Processing at EMNLP 2020 as a virtual event. The SDP workshop consisted of a research track (including a poster session), two invited talks and three Shared Tasks (CL-SciSumm, Lay-Summ and LongSumm), geared towards easier access to scientific methods and results. Website: https://ornlcda.github.io/SDProc

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Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm
Muthu Kumar Chandrasekaran | Guy Feigenblat | Eduard Hovy | Abhilasha Ravichander | Michal Shmueli-Scheuer | Anita de Waard
Proceedings of the First Workshop on Scholarly Document Processing

We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm. We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks. In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.

2019

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A Summarization System for Scientific Documents
Shai Erera | Michal Shmueli-Scheuer | Guy Feigenblat | Ora Peled Nakash | Odellia Boni | Haggai Roitman | Doron Cohen | Bar Weiner | Yosi Mass | Or Rivlin | Guy Lev | Achiya Jerbi | Jonathan Herzig | Yufang Hou | Charles Jochim | Martin Gleize | Francesca Bonin | Francesca Bonin | David Konopnicki
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations

We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.

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TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks
Guy Lev | Michal Shmueli-Scheuer | Jonathan Herzig | Achiya Jerbi | David Konopnicki
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Currently, no large-scale training data is available for the task of scientific paper summarization. In this paper, we propose a novel method that automatically generates summaries for scientific papers, by utilizing videos of talks at scientific conferences. We hypothesize that such talks constitute a coherent and concise description of the papers’ content, and can form the basis for good summaries. We collected 1716 papers and their corresponding videos, and created a dataset of paper summaries. A model trained on this dataset achieves similar performance as models trained on a dataset of summaries created manually. In addition, we validated the quality of our summaries by human experts.

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Bot2Vec: Learning Representations of Chatbots
Jonathan Herzig | Tommy Sandbank | Michal Shmueli-Scheuer | David Konopnicki
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Chatbots (i.e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms. These platforms are equipped with novel testing tools aimed at improving the quality of individual chatbots. Doing so requires an understanding of what sort of bots are being built (captured by their underlying conversation graphs) and how well they perform (derived through analysis of conversation logs). In this paper, we propose a new model, Bot2Vec, that embeds bots to a compact representation based on their structure and usage logs. Then, we utilize Bot2Vec representations to improve the quality of two bot analysis tasks. Using conversation data and graphs of over than 90 bots, we show that Bot2Vec representations improve detection performance by more than 16% for both tasks.

2018

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Detecting Egregious Conversations between Customers and Virtual Agents
Tommy Sandbank | Michal Shmueli-Scheuer | Jonathan Herzig | David Konopnicki | John Richards | David Piorkowski
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Virtual agents are becoming a prominent channel of interaction in customer service. Not all customer interactions are smooth, however, and some can become almost comically bad. In such instances, a human agent might need to step in and salvage the conversation. Detecting bad conversations is important since disappointing customer service may threaten customer loyalty and impact revenue. In this paper, we outline an approach to detecting such egregious conversations, using behavioral cues from the user, patterns in agent responses, and user-agent interaction. Using logs of two commercial systems, we show that using these features improves the detection F1-score by around 20% over using textual features alone. In addition, we show that those features are common across two quite different domains and, arguably, universal.

2017

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Neural Response Generation for Customer Service based on Personality Traits
Jonathan Herzig | Michal Shmueli-Scheuer | Tommy Sandbank | David Konopnicki
Proceedings of the 10th International Conference on Natural Language Generation

We present a neural response generation model that generates responses conditioned on a target personality. The model learns high level features based on the target personality, and uses them to update its hidden state. Our model achieves performance improvements in both perplexity and BLEU scores over a baseline sequence-to-sequence model, and is validated by human judges.

2016

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Classifying Emotions in Customer Support Dialogues in Social Media
Jonathan Herzig | Guy Feigenblat | Michal Shmueli-Scheuer | David Konopnicki | Anat Rafaeli | Daniel Altman | David Spivak
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue