Mir Tafseer Nayeem


2018

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Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion
Mir Tafseer Nayeem | Tanvir Ahmed Fuad | Yllias Chali
Proceedings of the 27th International Conference on Computational Linguistics

In this work, we aim at developing an unsupervised abstractive summarization system in the multi-document setting. We design a paraphrastic sentence fusion model which jointly performs sentence fusion and paraphrasing using skip-gram word embedding model at the sentence level. Our model improves the information coverage and at the same time abstractiveness of the generated sentences. We conduct our experiments on the human-generated multi-sentence compression datasets and evaluate our system on several newly proposed Machine Translation (MT) evaluation metrics. Furthermore, we apply our sentence level model to implement an abstractive multi-document summarization system where documents usually contain a related set of sentences. We also propose an optimal solution for the classical summary length limit problem which was not addressed in the past research. For the document level summary, we conduct experiments on the datasets of two different domains (e.g., news article and user reviews) which are well suited for multi-document abstractive summarization. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods.

2017

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Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging
Yllias Chali | Moin Tanvee | Mir Tafseer Nayeem
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

We propose a submodular function-based summarization system which integrates three important measures namely importance, coverage, and non-redundancy to detect the important sentences for the summary. We design monotone and submodular functions which allow us to apply an efficient and scalable greedy algorithm to obtain informative and well-covered summaries. In addition, we integrate two abstraction-based methods namely sentence compression and merging for generating an abstractive sentence set. We design our summarization models for both generic and query-focused summarization. Experimental results on DUC-2004 and DUC-2007 datasets show that our generic and query-focused summarizers have outperformed the state-of-the-art summarization systems in terms of ROUGE-1 and ROUGE-2 recall and F-measure.

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Extract with Order for Coherent Multi-Document Summarization
Mir Tafseer Nayeem | Yllias Chali
Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing

In this work, we aim at developing an extractive summarizer in the multi-document setting. We implement a rank based sentence selection using continuous vector representations along with key-phrases. Furthermore, we propose a model to tackle summary coherence for increasing readability. We conduct experiments on the Document Understanding Conference (DUC) 2004 datasets using ROUGE toolkit. Our experiments demonstrate that the methods bring significant improvements over the state of the art methods in terms of informativity and coherence.