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20162023
most citedA Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

99 citations · 123 across the 7 of their papers we have counts for

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Showing cs.CLShow all

11 papers · 1 filter

cs.CL2023

Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning

Alexander Hanbo Li, Mingyue Shang, Evangelia Spiliopoulou +9

We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data. Our…

cs.CL20221 cited

Novel Chapter Abstractive Summarization using Spinal Tree Aware Sub-Sentential Content Selection

Hardy Hardy, Miguel Ballesteros, Faisal Ladhak +3

Summarizing novel chapters is a difficult task due to the input length and the fact that sentences that appear in the desired summaries draw content from multiple places throughout…

cs.CL20221 cited

Synthetic Target Domain Supervision for Open Retrieval QA

Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan +5

Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) -- a state-of-the-art…

cs.CL20212 cited

Towards Robust Neural Retrieval Models with Synthetic Pre-Training

Revanth Gangi Reddy, Vikas Yadav, Md Arafat Sultan +4

Recent work has shown that commonly available machine reading comprehension (MRC) datasets can be used to train high-performance neural information retrieval (IR) systems. However,…

cs.CL202017 cited

End-to-End QA on COVID-19: Domain Adaptation with Synthetic Training

Revanth Gangi Reddy, Bhavani Iyer, Md Arafat Sultan +5

End-to-end question answering (QA) requires both information retrieval (IR) over a large document collection and machine reading comprehension (MRC) on the retrieved passages. Rece…

cs.CL2020

Answer Span Correction in Machine Reading Comprehension

Revanth Gangi Reddy, Md Arafat Sultan, Efsun Sarioglu Kayi +3

Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assess…