30 citations · 44 across the 7 of their papers we have counts for
7 papers
Learning to Rank Salient Content for Query-focused Summarization
Sajad Sotudeh, Nazli Goharian
This study examines the potential of integrating Learning-to-Rank (LTR) with Query-focused Summarization (QFS) to enhance the summary relevance via content prioritization. Using a…
LexBoost: Improving Lexical Document Retrieval with Nearest Neighbors
Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder +1
Sparse retrieval methods like BM25 are based on lexical overlap, focusing on the surface form of the terms that appear in the query and the document. The use of inverted indices in…
Genetic Approach to Mitigate Hallucination in Generative IR
Hrishikesh Kulkarni, Nazli Goharian, Ophir Frieder +1
Generative language models hallucinate. That is, at times, they generate factually flawed responses. These inaccuracies are particularly insidious because the responses are fluent…
Lexically-Accelerated Dense Retrieval
Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian +1
Retrieval approaches that score documents based on learned dense vectors (i.e., dense retrieval) rather than lexical signals (i.e., conventional retrieval) are increasingly popular…
QontSum: On Contrasting Salient Content for Query-focused Summarization
Sajad Sotudeh, Nazli Goharian
Query-focused summarization (QFS) is a challenging task in natural language processing that generates summaries to address specific queries. The broader field of Generative Informa…
Curriculum-Guided Abstractive Summarization
Sajad Sotudeh, Hanieh Deilamsalehy, Franck Dernoncourt +1
Recent Transformer-based summarization models have provided a promising approach to abstractive summarization. They go beyond sentence selection and extractive strategies to deal w…