Papers by David Konopnicki
An Editorial Network for Enhanced Document Summarization (D19-54)
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| Challenge: | Existing extractive and abstractive summarization methods are less fluent, coherent and readable, whereas extractive methods are sensitive to vocabulary size, making them more difficult to train and generalize. |
| Approach: | They propose an approach which uses a combination of extractive and abstractive methods to combine a given sequence of sentences into a short version. |
| Outcome: | The proposed method is compared with state-of-the-art methods using extractive-only or abstractive- only baselines. |
Agent Assist through Conversation Analysis (2020.emnlp-demos)
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Kshitij Fadnis, Nathaniel Mills, Jatin Ganhotra, Haggai Roitman, Gaurav Pandey, Doron Cohen, Yosi Mass, Shai Erera, Chulaka Gunasekara, Danish Contractor, Siva Patel, Q. Vera Liao, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using conversational approach to information retrieval for agent assistance, customer support agents are a critical part of an organization's customer support team. |
| Approach: | They propose a conversational approach to information retrieval for agent assistance that monitors an evolving conversation and recommends both responses and URLs of documents. |
| Outcome: | The proposed system monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. |
TWEETSUMM - A Dialog Summarization Dataset for Customer Service (2021.findings-emnlp)
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Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov
| Challenge: | a dataset focused on customer care dialog summarization is the first to focus on real-world customer care conversations . it contains extractive and abstractive summaries, and extractive summarizing methods are also introduced . |
| Approach: | They present a customer care dialog summarization dataset with 6500 human annotated summaries . they introduce an unsupervised method for extracting dialog summary data . |
| Outcome: | The proposed method is based on real-world customer support dialogs and includes extractive and abstractive summaries. |
TalkSumm: A Dataset and Scalable Annotation Method for Scientific Paper Summarization Based on Conference Talks (P19-1)
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| Challenge: | Currently, no large-scale training data is available for the task of scientific paper summarization. |
| Approach: | They propose a method that automatically generates scientific paper summaries by utilizing videos of scientific conferences. |
| Outcome: | The proposed model performs similar to models trained on a dataset of summaries created manually. |
Conversational Document Prediction to Assist Customer Care Agents (2020.emnlp-main)
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Jatin Ganhotra, Haggai Roitman, Doron Cohen, Nathaniel Mills, Chulaka Gunasekara, Yosi Mass, Sachindra Joshi, Luis Lastras, David Konopnicki
| Challenge: | Using a conversational search system, the agent/system can ask clarification questions and interactively modify the search results as the conversation progresses. |
| Approach: | They propose to use a public dataset to analyze the task of predicting the documents that customer care agents can use to facilitate users’ needs. |
| Outcome: | The proposed model is more efficient than existing models and is more cost-effective than existing ones. |
Unsupervised FAQ Retrieval with Question Generation and BERT (2020.acl-main)
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| Challenge: | Frequently Asked Questions (FAQ) retrieval requires labeled datasets for training neural models. |
| Approach: | They propose to exploit FAQ pairs to train two BERT models that match user queries to FAQ answers and questions. |
| Outcome: | The proposed model outperforms supervised models on existing datasets and is on par with existing dataset. |
Detecting Egregious Conversations between Customers and Virtual Agents (N18-1)
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Tommy Sandbank, Michal Shmueli-Scheuer, Jonathan Herzig, David Konopnicki, John Richards, David Piorkowski
| Challenge: | 80% of businesses plan to use chatbots by 2020, according to recent studies . but some bad conversations can be difficult to detect and could lead to litigation . |
| Approach: | They propose a method to detect bad conversations using behavioral cues from the user and patterns in agent responses. |
| Outcome: | The proposed method improves the detection F1 score by 20% over textual features. |
A Summarization System for Scientific Documents (D19-3)
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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
| Challenge: | a qualitative user study identified the most valuable scenarios for scientific content consumption. |
| Approach: | They propose a system that retrieves and summarizes scientific documents for a given information need. |
| Outcome: | The proposed system ingested 270,000 scientific papers and validated with human experts. |
Summary Grounded Conversation Generation (2021.findings-acl)
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| Challenge: | Existing datasets for conversation summarization are small due to the lack of large-scale datasets. |
| Approach: | They propose three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. |
| Outcome: | The proposed models can generate entire conversations with only a summary of a conversation as the input. |
Learning Concept Abstractness Using Weak Supervision (D18-1)
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Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim
| Challenge: | Existing methods for inferring abstractness of words and expressions without labeled data are limited and limited. |
| Approach: | They propose a weakly supervised approach for inferring the property of abstractness of words and expressions in the absence of labeled data. |
| Outcome: | The proposed approach obtains high correlation with human labels in the absence of labeled data. |