10 citations · 17 across the 4 of their papers we have counts for
6 papers
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Demian Gholipour Ghalandari, Chris Hokamp, Georgiana Ifrim
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality. Unsupervised objective driven methods for sen…
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Chris Hokamp, Demian Gholipour Ghalandari, Nghia The Pham +1
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing. However, some applications, such as multi-document summarization, multi-modal mac…
Examining the State-of-the-Art in News Timeline Summarization
Demian Gholipour Ghalandari, Georgiana Ifrim
Previous work on automatic news timeline summarization (TLS) leaves an unclear picture about how this task can generally be approached and how well it is currently solved. This is…
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Demian Gholipour Ghalandari, Chris Hokamp, Nghia The Pham +2
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds,…
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
Chris Hokamp, John Glover, Demian Gholipour
We study several methods for full or partial sharing of the decoder parameters of multilingual NMT models. We evaluate both fully supervised and zero-shot translation performance i…
Revisiting the Centroid-based Method: A Strong Baseline for Multi-Document Summarization
Demian Gholipour Ghalandari
The centroid-based model for extractive document summarization is a simple and fast baseline that ranks sentences based on their similarity to a centroid vector. In this paper, we…