1,673 research outputs found
Novel Word Embedding and Translation-based Language Modeling for Extractive Speech Summarization
Word embedding methods revolve around learning continuous distributed vector
representations of words with neural networks, which can capture semantic
and/or syntactic cues, and in turn be used to induce similarity measures among
words, sentences and documents in context. Celebrated methods can be
categorized as prediction-based and count-based methods according to the
training objectives and model architectures. Their pros and cons have been
extensively analyzed and evaluated in recent studies, but there is relatively
less work continuing the line of research to develop an enhanced learning
method that brings together the advantages of the two model families. In
addition, the interpretation of the learned word representations still remains
somewhat opaque. Motivated by the observations and considering the pressing
need, this paper presents a novel method for learning the word representations,
which not only inherits the advantages of classic word embedding methods but
also offers a clearer and more rigorous interpretation of the learned word
representations. Built upon the proposed word embedding method, we further
formulate a translation-based language modeling framework for the extractive
speech summarization task. A series of empirical evaluations demonstrate the
effectiveness of the proposed word representation learning and language
modeling techniques in extractive speech summarization
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information
Text summarization is the research area aiming at creating a short and
condensed version of the original document, which conveys the main idea of the
document in a few words. This research topic has started to attract the
attention of a large community of researchers, and it is nowadays counted as
one of the most promising research areas. In general, text summarization
algorithms aim at using a plain text document as input and then output a
summary. However, in real-world applications, most of the data is not in a
plain text format. Instead, there is much manifold information to be
summarized, such as the summary for a web page based on a query in the search
engine, extreme long document (e.g., academic paper), dialog history and so on.
In this paper, we focus on the survey of these new summarization tasks and
approaches in the real-world application.Comment: Accepted by IJCAI 2020 Survey Trac
Attention-based Neural Text Segmentation
Text segmentation plays an important role in various Natural Language
Processing (NLP) tasks like summarization, context understanding, document
indexing and document noise removal. Previous methods for this task require
manual feature engineering, huge memory requirements and large execution times.
To the best of our knowledge, this paper is the first one to present a novel
supervised neural approach for text segmentation. Specifically, we propose an
attention-based bidirectional LSTM model where sentence embeddings are learned
using CNNs and the segments are predicted based on contextual information. This
model can automatically handle variable sized context information. Compared to
the existing competitive baselines, the proposed model shows a performance
improvement of ~7% in WinDiff score on three benchmark datasets
Deconvolutional Paragraph Representation Learning
Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the length of the text. We propose a sequence-to-sequence, purely
convolutional and deconvolutional autoencoding framework that is free of the
above issue, while also being computationally efficient. The proposed method is
simple, easy to implement and can be leveraged as a building block for many
applications. We show empirically that compared to RNNs, our framework is
better at reconstructing and correcting long paragraphs. Quantitative
evaluation on semi-supervised text classification and summarization tasks
demonstrate the potential for better utilization of long unlabeled text data.Comment: Accepted by NIPS 201
An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features
Tremendous amounts of multimedia associated with speech information are
driving an urgent need to develop efficient and effective automatic
summarization methods. To this end, we have seen rapid progress in applying
supervised deep neural network-based methods to extractive speech
summarization. More recently, the Bidirectional Encoder Representations from
Transformers (BERT) model was proposed and has achieved record-breaking success
on many natural language processing (NLP) tasks such as question answering and
language understanding. In view of this, we in this paper contextualize and
enhance the state-of-the-art BERT-based model for speech summarization, while
its contributions are at least three-fold. First, we explore the incorporation
of confidence scores into sentence representations to see if such an attempt
could help alleviate the negative effects caused by imperfect automatic speech
recognition (ASR). Secondly, we also augment the sentence embeddings obtained
from BERT with extra structural and linguistic features, such as sentence
position and inverse document frequency (IDF) statistics. Finally, we validate
the effectiveness of our proposed method on a benchmark dataset, in comparison
to several classic and celebrated speech summarization methods.Comment: Accepted by EUSIPCO 202
Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations
Due to the lack of publicly available resources, conversation summarization
has received far less attention than text summarization. As the purpose of
conversations is to exchange information between at least two interlocutors,
key information about a certain topic is often scattered and spanned across
multiple utterances and turns from different speakers. This phenomenon is more
pronounced during spoken conversations, where speech characteristics such as
backchanneling and false-starts might interrupt the topical flow. Moreover,
topic diffusion and (intra-utterance) topic drift are also more common in
human-to-human conversations. Such linguistic characteristics of dialogue
topics make sentence-level extractive summarization approaches used in spoken
documents ill-suited for summarizing conversations. Pointer-generator networks
have effectively demonstrated its strength at integrating extractive and
abstractive capabilities through neural modeling in text summarization. To the
best of our knowledge, to date no one has adopted it for summarizing
conversations. In this work, we propose a topic-aware architecture to exploit
the inherent hierarchical structure in conversations to further adapt the
pointer-generator model. Our approach significantly outperforms competitive
baselines, achieves more efficient learning outcomes, and attains more robust
performance.Comment: To appear in ASRU201
Leveraging Word Embeddings for Spoken Document Summarization
Owing to the rapidly growing multimedia content available on the Internet,
extractive spoken document summarization, with the purpose of automatically
selecting a set of representative sentences from a spoken document to concisely
express the most important theme of the document, has been an active area of
research and experimentation. On the other hand, word embedding has emerged as
a newly favorite research subject because of its excellent performance in many
natural language processing (NLP)-related tasks. However, as far as we are
aware, there are relatively few studies investigating its use in extractive
text or speech summarization. A common thread of leveraging word embeddings in
the summarization process is to represent the document (or sentence) by
averaging the word embeddings of the words occurring in the document (or
sentence). Then, intuitively, the cosine similarity measure can be employed to
determine the relevance degree between a pair of representations. Beyond the
continued efforts made to improve the representation of words, this paper
focuses on building novel and efficient ranking models based on the general
word embedding methods for extractive speech summarization. Experimental
results demonstrate the effectiveness of our proposed methods, compared to
existing state-of-the-art methods
Focused Meeting Summarization via Unsupervised Relation Extraction
We present a novel unsupervised framework for focused meeting summarization
that views the problem as an instance of relation extraction. We adapt an
existing in-domain relation learner (Chen et al., 2011) by exploiting a set of
task-specific constraints and features. We evaluate the approach on a decision
summarization task and show that it outperforms unsupervised utterance-level
extractive summarization baselines as well as an existing generic
relation-extraction-based summarization method. Moreover, our approach produces
summaries competitive with those generated by supervised methods in terms of
the standard ROUGE score.Comment: SIGDIAL 201
Neural Discourse Modeling of Conversations
Deep neural networks have shown recent promise in many language-related tasks
such as the modeling of conversations. We extend RNN-based sequence to sequence
models to capture the long range discourse across many turns of conversation.
We perform a sensitivity analysis on how much additional context affects
performance, and provide quantitative and qualitative evidence that these
models are able to capture discourse relationships across multiple utterances.
Our results quantifies how adding an additional RNN layer for modeling
discourse improves the quality of output utterances and providing more of the
previous conversation as input also improves performance. By searching the
generated outputs for specific discourse markers we show how neural discourse
models can exhibit increased coherence and cohesion in conversations
A Survey on Dialogue Summarization: Recent Advances and New Frontiers
With the development of dialogue systems and natural language generation
techniques, the resurgence of dialogue summarization has attracted significant
research attentions, which aims to condense the original dialogue into a
shorter version covering salient information. However, there remains a lack of
comprehensive survey for this task. To this end, we take the first step and
present a thorough review of this research field. In detail, we provide an
overview of publicly available research datasets, summarize existing works
according to the domain of input dialogue as well as organize leaderboards
under unified metrics. Furthermore, we discuss some future directions and give
our thoughts. We hope that this first survey of dialogue summarization can
provide the community with a quick access and a general picture to this task
and motivate future researches
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