Publications

  • Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus and Julian McAuley. Large Language Models as Zero-Shot Conversational Recommenders CIKM, 2023. (PDF)
  • Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, Yejin Choi, Jianfeng Gao. Go-Figure: A Meta Evaluation of Factuality in Summarization. ACL, 2021. (Preprint PDF)
  • Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed H. Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, Dragomir Radev. QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization. NAACL, 2021. (PDF)
  • Keping Bi, Rahul Jha, W. Bruce Croft and Asli Çelikyilmaz. AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document Summarization. EACL, 2021. (PDF)
  • Rahul Jha, Keping Bi, Yang Li, Mahdi Pakdaman, Asli Celikyilmaz, Ivan Zhiboedov, Kieran McDonald. Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization. EMNLP Workshop on Evaluation and Comparison for NLP systems (2020). (PDF)
  • Kunho Kim, Rahul Jha, Kyle Williams, Alex Marin and Imed Zitouni, Slot Tagging for Task Oriented Spoken Language Understanding in Human-to-human Conversation Scenarios. CoNLL, 2019 (PDF)
  • Sungjin Lee and Rahul Jha. Zero-Shot Adaptive Transfer for Conversational Language Understanding. AAAI, 2019 (PDF)
  • Rahul Jha, Alex Marin, Suvamsh Shivaprasad and Imed Zitouni. Bag of Experts Architectures for Model Reuse in Conversational Language Understanding. NAACL, 2018. (PDF)
  • Rahul Jha, Amjad Abu-Jbara, Vahed Qazvinian and Dragomir Radev. NLP Driven Citation Analysis for Scientometrics. Journal of Natural Language Engineering, 2016. (PDF)
  • Rahul Jha, Catherine Finegan-Dollak, Ben King, Reed Coke and Dragomir Radev. Content Models for Survey Generation: A Factoid-Based Evaluation. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2015. (PDF)
  • Rahul Jha, Reed Coke and Dragomir Radev. Surveyor: A system for generating coherent survey articles for scientific topics. In Proceedings of Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. (PDF)
  • Rahul Jha, Amjad Abu-Jbara and Dragomir Radev. A system for summarizing scientific topics starting from keywords. In Proceedings of The Association for Computational Linguistics, 2013. (PDF)
  • Dragomir Radev, Amanda Stent, Joel Tetreault, Aasish Pappu, Aikaterini Iliakopoulou, Agustin Chanfreau, Paloma de Juan, Jordi Vallmitjana, Alejandro Jaimes, Rahul Jha, Bob Mankoff. Humor in collective discourse: Unsupervised funniness detection in the new yorker cartoon caption contest. LREC, 2016 (PDF)
  • Kokil Jaidka, Muthu Kumar Chandrasekaran, Rahul Jha, Christopher Jones, Min-Yen Kan, Ankur Khanna, Diego Molla-Aliod, Dragomir R. Radev, Francesco Ronzano, and Horacio Saggion. The computational linguistics summarization pilot task. In Proceedings of TAC, 2014. (PDF)
  • Benjamin King, Rahul Jha, and Dragomir Radev. Heterogeneous Networks and Their Applications: Scientometrics, Name Disambiguation, and Topic Modeling. In Transactions of the Association for Computational Linguistics, 2014. (PDF)
  • Benjamin King, Rahul Jha, and Dragomir Radev. Random walk factoid annotation for collective discourse. In Proceedings of The Association for Computational Linguistics, 2013. (PDF)
  • Amjad Abu-Jbara, Rahul Jha, Eric Morley, and Dragomir Radev. Experimental results on the native language identification shared task. In Proceedings of The NAACL 2013 Workshop on Native Language Identification, 2013. (PDF)
  • Rahul Jha and Dragomir Radev. An unsupervised method for learning probabilistic first order logic models from unstructured clinical text. In ICML Workshop on Learning from Unstructured Clinical Text, Bellevue, Washington, USA, July 2nd, 2011. (PDF)
  • Ahmed Hassan, Amjad Abu-Jbara, Rahul Jha and Dragomir Radev. Identifying the semantic orientation of foreign words. in 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon, USA, June 19-24, 2011. (PDF)

PHD Thesis

NLP Driven Models for Automatically Generating Survey Articles for Scientific Topics

This thesis presents new methods that use natural language processing (NLP) driven models for summarizing research in scientific fields. Given a topic query in the form of a text string, we present methods for finding research articles relevant to the topic as well as summarization algorithms that use lexical and discourse information present in the text of these articles to generate coherent and readable extractive summaries of past research on the topic. In addition to summarizing prior research, good survey articles should also forecast future trends. With this motivation, we present work on forecasting future impact of scientific publications using NLP driven features.

Official version, Singled Spaced version