5,450 research outputs found
Query-Based Summarization using Rhetorical Structure Theory
Research on Question Answering is focused mainly on classifying the question type and finding
the answer. Presenting the answer in a way that suits the userâs needs has received little
attention. This paper shows how existing question answering systemsâwhich aim at finding
precise answers to questionsâcan be improved by exploiting summarization techniques to extract
more than just the answer from the document in which the answer resides. This is done
using a graph search algorithm which searches for relevant sentences in the discourse structure,
which is represented as a graph. The Rhetorical Structure Theory (RST) is used to create a
graph representation of a text document. The output is an extensive answer, which not only
answers the question, but also gives the user an opportunity to assess the accuracy of the answer
(is this what I am looking for?), and to find additional information that is related to the question,
and which may satisfy an information need. This has been implemented in a working multimodal
question answering system where it operates with two independently developed question
answering modules
Graph-based Neural Multi-Document Summarization
We propose a neural multi-document summarization (MDS) system that
incorporates sentence relation graphs. We employ a Graph Convolutional Network
(GCN) on the relation graphs, with sentence embeddings obtained from Recurrent
Neural Networks as input node features. Through multiple layer-wise
propagation, the GCN generates high-level hidden sentence features for salience
estimation. We then use a greedy heuristic to extract salient sentences while
avoiding redundancy. In our experiments on DUC 2004, we consider three types of
sentence relation graphs and demonstrate the advantage of combining sentence
relations in graphs with the representation power of deep neural networks. Our
model improves upon traditional graph-based extractive approaches and the
vanilla GRU sequence model with no graph, and it achieves competitive results
against other state-of-the-art multi-document summarization systems.Comment: In CoNLL 201
A Novel ILP Framework for Summarizing Content with High Lexical Variety
Summarizing content contributed by individuals can be challenging, because
people make different lexical choices even when describing the same events.
However, there remains a significant need to summarize such content. Examples
include the student responses to post-class reflective questions, product
reviews, and news articles published by different news agencies related to the
same events. High lexical diversity of these documents hinders the system's
ability to effectively identify salient content and reduce summary redundancy.
In this paper, we overcome this issue by introducing an integer linear
programming-based summarization framework. It incorporates a low-rank
approximation to the sentence-word co-occurrence matrix to intrinsically group
semantically-similar lexical items. We conduct extensive experiments on
datasets of student responses, product reviews, and news documents. Our
approach compares favorably to a number of extractive baselines as well as a
neural abstractive summarization system. The paper finally sheds light on when
and why the proposed framework is effective at summarizing content with high
lexical variety.Comment: Accepted for publication in the journal of Natural Language
Engineering, 201
Foreground and background text in retrieval
Our hypothesis is that certain clauses have foreground functions in text,
while other clauses have background functions and that these functions are
expressed or reflected in the syntactic structure of the clause.
Presumably these clauses will have differing utility for automatic
approaches to text understanding; a summarization system might want to
utilize background clauses to capture commonalities between numbers of
documents while an indexing system might use foreground clauses in order to
capture specific characteristics of a certain document
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