Private industry datasets and public records contain more information than any algorithm can efficiently process or any person can reasonably interpret. This is a basic problem faced by researchers in visual analytics. Graph visualizations (a common large dataset representation) can organize relationships and entities in a visually accessible manner. Our work applies graph sampling and summariz...

Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results...

Extractive text summarization selects the most important sentences from a document, preserves their original meaning, and produces an objective fact-based summary. It is faster less computationally intensive than abstract techniques. Learning cross-sentence relationships crucial for extractive summarization. However, of language models currently in use process data sequentially, which makes it ...

In this paper, we study the problem of summarizing email conversations. We first build a sentence quotation graph that captures the conversation structure among emails. We adopt three cohesion measures: clue words, semantic similarity and cosine similarity as the weight of the edges. Second, we use two graph-based summarization approaches, Generalized ClueWordSummarizer and PageRank, to extract...

With an ever increasing size of text present on the Internet, automatic summary generation remains an important problem for natural language understanding. In this work we explore a novel full-fledged pipeline for text summarization with an intermediate step of Abstract Meaning Representation (AMR). The pipeline proposed by us first generates an AMR graph of an input story, through which it ext...

In this report, we present details about the participation of WHUSUM in the guided summarization track at TAC 2011. Guided summarization task requires participants to produce short, coherent summaries of news articles with the guidance of predefined categories and aspects for each category. This year, we extended our query-focused update summarization system with aspect related information. In ...

Text summarization task is still an active area of research in natural language preprocessing. Several methods that have been proposed in the literature to solve this task have presented mixed success. However, such methods developed in a multi-document Arabic text summarization are based on extractive summary and none of them is oriented to abstractive summary. This is due to the challenges of...

Text coherence evaluation becomes a vital and lovely task in Natural Language Processing subfields, such as text summarization, question answering, text generation and machine translation. Existing methods like entity-based and graph-based models are engaging with nouns and noun phrases change role in sequential sentences within short part of a text. They even have limitations in global coheren...