Faiaz Rahman


2021

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DART: Open-Domain Structured Data Record to Text Generation
Linyong Nan | Dragomir Radev | Rui Zhang | Amrit Rau | Abhinand Sivaprasad | Chiachun Hsieh | Xiangru Tang | Aadit Vyas | Neha Verma | Pranav Krishna | Yangxiaokang Liu | Nadia Irwanto | Jessica Pan | Faiaz Rahman | Ahmad Zaidi | Mutethia Mutuma | Yasin Tarabar | Ankit Gupta | Tao Yu | Yi Chern Tan | Xi Victoria Lin | Caiming Xiong | Richard Socher | Nazneen Fatema Rajani
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present DART, an open domain structured DAta Record to Text generation dataset with over 82k instances (DARTs). Data-to-text annotations can be a costly process, especially when dealing with tables which are the major source of structured data and contain nontrivial structures. To this end, we propose a procedure of extracting semantic triples from tables that encodes their structures by exploiting the semantic dependencies among table headers and the table title. Our dataset construction framework effectively merged heterogeneous sources from open domain semantic parsing and spoken dialogue systems by utilizing techniques including tree ontology annotation, question-answer pair to declarative sentence conversion, and predicate unification, all with minimum post-editing. We present systematic evaluation on DART as well as new state-of-the-art results on WebNLG 2017 to show that DART (1) poses new challenges to existing data-to-text datasets and (2) facilitates out-of-domain generalization. Our data and code can be found at https://github.com/Yale-LILY/dart.

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ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
Alexander Fabbri | Faiaz Rahman | Imad Rizvi | Borui Wang | Haoran Li | Yashar Mehdad | Dragomir Radev
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles. This research gap is due, in part, to the lack of standardized datasets for summarizing online discussions. To address this gap, we design annotation protocols motivated by an issues–viewpoints–assertions framework to crowdsource four new datasets on diverse online conversation forms of news comments, discussion forums, community question answering forums, and email threads. We benchmark state-of-the-art models on our datasets and analyze characteristics associated with the data. To create a comprehensive benchmark, we also evaluate these models on widely-used conversation summarization datasets to establish strong baselines in this domain. Furthermore, we incorporate argument mining through graph construction to directly model the issues, viewpoints, and assertions present in a conversation and filter noisy input, showing comparable or improved results according to automatic and human evaluations.