Papers by Haggai Roitman
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. |
HotelQuEST: Balancing Quality and Efficiency in Agentic Search (2026.eacl-industry)
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| Challenge: | Existing benchmarks for agentic search focus primarily on answer quality, overlooking efficiency factors that are critical for real-world deployment. |
| Approach: | They propose a benchmark for hotel search queries that includes 214 hotel query queries that range from simple factual requests to complex queries. |
| Outcome: | The proposed benchmarks show that LLM-based agents achieve higher accuracy than traditional retrievers, but at substantially higher costs due to redundant tool calls and suboptimal routing that fails to match query complexity to model capability. |
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. |
Ad-hoc Document Retrieval using Weak-Supervision with BERT and GPT2 (2020.emnlp-main)
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| Challenge: | a weakly-supervised method is used for document retrieval tasks . traditional methods are used for ad-hoc querying, but they require large amounts of labeled data . |
| Approach: | They propose a weakly-supervised method for training deep learning models for ad-hoc document retrieval using weak-supervision from the documents in the corpus. |
| Outcome: | The proposed method outperforms state-of-the-art methods on a COVID-19 dataset and two news datasets without the need for labeling data. |
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. |
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. |