INCREMENTAL SENTENCE COMPRESSION USING LSTM RECURRENT NETWORKS
Sakriani Sakti1 , Faiz Ilham1,2 , Graham Neubig1 , Tomoki Toda1∗ ,Ayu Purwarianti2 , Satoshi Nakamura1
1

2

Graduate School of Information Science, Nara Institute of Science and Technology, Japan
School of Electrical Engineering and Informatics, Bandung Institute of Technology, Indonesia{ssakti,neubig,tomoki,s-nakamura}@is.naist.jp,{13511080@std.,ayu@}stei.itb.ac.id

ABSTRACT

Sentence compression is a text-to-text rewriting methodthat produces a shortened form of a sentence while retaining the most important information [2]. Research on sentence compression has been extensively pursued across different modeling paradigms. Specifically, extractive sentencecompression focuses on word deletion; a target compressed
sentence is formed by dropping any subset of words from the
input sentence [3]. Many solutions to the compression problem have been cast as a translation task between two sentencesin the same language based on a parallel corpus of original
and compressed sentences.

Many of the current sentence compression techniques attempt to produce a shortened form of a sentence by relying
on syntactic structure such as dependency tree representations. While the performance of sentence compression has
been improving, these approaches require a full parse of thesentence before performing sentence compression, making it
difficult to perform compression in real time. In this paper,
we examine the possibilities of performing incremental sentence compression using long short-term memory (LSTM)
recurrent neural networks (RNN). The decision of whether
to remove a word is done at each time step, without waiting for the end of the sentence. Various RNN parameters areinvestigated, including the number of layers and network connections. Furthermore, we also propose using a pretraining
method in which the network is pretrained as an autoencoder.
Experimental results reveal that our method obtains compression rates similar to human references and a better accuracy
than the state-of-the-art tree transduction models.
Index Terms— Sentence compression, recurrent neuralnetwork, long short term memory

To date, the work by Knight and Marcu [3] proposed anoisy-channel formulation using a Synchronous Context-Free
Grammar (SCFG) [4, 5], and a variety of improvements exist
including those capable of handling syntactically complex expressions [6] and head-driven Markovization of SCFG deletion rules [7]. A number of other approaches also exist in
the speech domain; Clarke and Lapata [8] use integer linear
programming (ILP) to infer globally optimal compressions in
broadcast news, and Liu and Liu [9] model the speech utterance compression task as a sequence labeling problem using conditional random fields (CRF). However, one notable
method in extractive sentence compression is the tree transduction formulation by Cohn and Lapata [10, 11, 12]. In thismethod, sentence compression is seen as rewriting a parse tree
into a smaller one. Specifically, the model adopts the synchronous tree substitution grammar (STSG) formalism [6],which can construct non-isomorphic tree structures while using efficient inference algorithms.

1. INTRODUCTION
Today, there is increasing demand for real-time broadcast
closed captioning services that can convey spoken utterances
using text on a television, video screen, or other visual display with minimum delay. These closed captions are mostly
created for deaf and hard-of-hearing individuals to assist incomprehension [1]. Since the text information needs to reachthe audience within the time the relevant video information
is displayed on the screen, the closed caption captioning
process has to be done simultaneously with the visual andaudio display. However, it is often the case that the number of spoken words that need to be transcribed exceeds the
maximum number of words that people can read in real time
on a TV screen. Therefore, the development of an automatic
closed captioning system requires not only high-speed speech
recognition but also real-time sentence compression.

While the performance of sentence compression has been
improving, these approaches require a full parse of the sentence before performing sentence compression, making it difficult to perform compression as part of a real-time closedcaptioning process. The motivation in the current study is to
examine the possibilities of performing incremental sentencecompression using recurrent neural networks (RNN). The decision of whether to remove a word is done at each time step,
without waiting for the end of the sentence. In addition, because the available parallel training data for the sentence compression task is relatively small, we also propose a method to∗ He is now at Information Technology Center, Nagoya University, Japan(email: tomoki@icts.nagoya-u.ac.jp).

978-1-4799-7291-3/15/$31.00 ©2015 IEEE

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ASRU 2015

Parameters bh and by are bias vectors connected to hidden andoutput units, respectively. Here, h0 as a base for recursion isassumed to be 0.
Training the RNN is done using the back-propagation
through time (BPTT) algorithm [24, 25], which is a modification of the conventional back-propagation algorithm usedin feed-forward neural network training, while sharing theconnection weights across different time steps. Here, three
time-steps are used in the unfolding process.

use pretraining to learn representations of the sentence usinglarge unsupervised data before fine-tuning the sentence compression data.
2. RELATED WORKS
An RNN is a type of neural network that has feedback connections, such that in each time-step layers in the net will also
receive inputs by using the output values at the previous time
step. Consequently RNNs can be considered deep neural networks (DNNs) with the number of layers being proportional
to the number of time steps. These additional feedback connections allow RNNs to remember and process informationfrom previous time steps, making them a powerful and expressive model for sequential tasks.
To date, RNNs have provided advantages in various
speech and language processing tasks. One of the first application of RNNs in speech processing was to use it as phonemodeling and phone probability estimation [13, 14]. RNNs
have also been widely applied for text classification [15].
Perhaps the most successful application of RNNs in recent
years has been their use in RNN language models (LMs) [16],which have demonstrated outstanding performance in a variety of tasks, including speech recognition [16, 17], machinetranslation [18], and learning word embeddings [19, 20].
More recently, many research works have focused on utilizing RNNs for spoken language understanding [21, 22].
However, to the authors’ knowledge, there has been noattempt to apply RNNs to solving the problem of sentencecompression. In this study, we utilize RNNs for incremental sentence compression. Specifically, we apply long shortterm memory (LSTM) RNNs to predict whether to remove
a word at each time step. Furthermore, we explore various
RNN structures and propose modifications to the standard
RNN layers as well as the use of pretraining methods.

Hidden

Input

xt

.
..

Output

ht
bh

by

Wxh

Why

ot
sigmoid..
.

softmax

ht-1
Whh

..
.
Copy

Fig. 1. Overview of RNNs.
In order to avoid the problem of vanishing gradients that
plague standard RNNs, we utilize LSTM cells [26], which arememory cells modulated with four gates: previous value input, input gate, output gate and forget gate. Because LSTM
cells have linear feedback loops between the states of multiple time steps, the error remains constant, allowing for more
effectively capturing long-distance dependencies.

3. COMPRESSION MODELING WITH LSTM-RNN

3.2. Various Network Structures

3.1. Basic LSTM-RNN

We explore various network structures and propose modifications to the basic LSTM-RNN layers as shown in Fig. 2:

In this sentence compression task, we utilize the classic Elman RNN architecture [23] shown in Fig. 1. Each layer represents a set of neurons, and the layers are connected withweights. The input layer xt represents the input word at timet, and the hidden layer ht is activated by the current input xt
and the previous hidden activation ht−1 :ht = sigmoid(Wxh xt + Whh ht−1 + bh ),• Basic LSTM (denoted as “LSTM-basic”)
We used a basic LSTM with one input layer, one output layer, and one hidden layer (including the recurrent
layer).
• LSTM with a continuous representation layer (denoted as “LSTM-basic+cont”)
To handle the sparseness of the input representation, we
extend the above LSTM structure to include a continuous representation layer placed between the input andhidden layers, with the same size as the hidden layer.
The motivation is to learn a better word representation
in a smooth continuous space. In this layer, the hyperbolic tangent tanh(Wxh xt + bh ) is applied.

(1)

where Wxh and Whh are the weight matrices between the input and hidden layers, and between the hidden and previous
hidden layers, respectively. The output determines the probability distribution over whether the word is important or unimportant:ot = softmax(Why ht + by ).
(2)

253

layer back to “LSTM-basic+cont+ext” and performed fine• LSTM with a continuous representation layer andextra connection (denoted as “LSTM-basic+cont+ext”) tuning. Fig. 3 illustrates the structure, denoted as “LSTMbasic+cont+ext+pae.”
To support the decision at the output layer, we further
extend the LSTM-basic+cont structure with an extra
We also attempted other methods where we pretrained
connection from the continuous representation layer tothe model as a language model that predicts the next word.
the output layer.
But the performance is below the performance obtained with
autoencoder pretraining. This might be because the method
tuned the network for the next word but not for the current(a) Basic LSTM (LSTM-basic)
word. Therefore, we decided to only use pretraining as an→ Denoted as “LSTM-basic”
autoencoder.
Input

HiddenOutput

4. EXPERIMENTAL SETUP
4.1. Corpora(b) LSTM with additional continuous representation layer

The experiments were conducted on CLwritten and CLspoken corpora1 , which are widely used for evaluating sentencecompression techniques [8]. Samples of both CLWritten and
CLSpoken are shown in Table 1. The former was created bysampling from written sources including the British National
Corpus (BNC) and the American News text corpus, while thelatter was created by manually transcribed speech of broadcast news stories. Since CLSpoken is created from a speech
corpus, it often contains incomplete and ungrammatical utterances and speech artifacts such as disfluencies, false starts
and hesitations.

→ Denoted as “LSTM-basic+cont”
Continuous
representation

Input

HiddenOutput(c) LSTM with additional continuous representation layerand direct connection→ Denoted as “LSTM-basic+cont+ext”
Continuous
representation

Input

HiddenOutputTable 1. CLWritten and CLSpoken sample data.

Fig. 2. Various RNN structures.

Data Set
LSTM with pretraining as auto-encoder (PAE)→ Denoted as “LSTM-basic+cont+ext+pae”

CLWritten

Pretraining:
Current
word

Continuous
representation

HiddenContinuous
representation

HiddenCurrent
word

Fine-tuning:
Input

Output

CLSpokenFig. 3. LSTM with pretraining method as an autoencoder.
3.3. Pretraining Method
Because the available parallel training data for the sentencecompression task is relatively small, we also propose amethod to use pretraining to learn representations of a sentence using large unsupervised data before fine-tuning thesentence compression data.
First, we modified the “LSTM-basic+cont+ext” design
so that the output layer had the same size as the input layer,
without recurrent and extra connections. Then, we pretrained it as an autoencoder that outputs exactly the samevalue as the input. After pretraining, we changed the output

Original
The powerful balanceof these figure compositions is highlighted
when they are transposed into tubes andsheets of metal.
Laurie had a warmthof personality in all
his doings with people
and politics.
All those things suggest to most votersthat the White Houseis hiding something.
Most
international
airports that service
large planes like thisare 4,000 meters, double in length, and it
may be a contributing
factor.

Compressed
The balance is highlighted when they aretransposed into tubes
and sheets of metal.
Laurie had a warmthwith people and politics.
Those things suggestthat the White Houseis hiding something.
Most
international
airports that service
large planes are 4,000
meters, and it may be
a contributing factor.

The corresponding shortened/compressed form of all sentences were created manually. Here, the annotators wereasked to produce the smallest possible target compression bydeleting extraneous words from the source, without changing
the word order and the meaning of the sentences.
1 CLWritten and CLSpoken: http://homepages.inf.ed.ac.uk/s0460084/data/254

training and testing the RNN model.

The total number of sentences and the corresponding
shortened/compressed pairs in CLWritten is 1433, whichwere taken from 82 articles, while CLSpoken consist of 1370
pairs taken from 50 articles. After some preprocessing, we
had 1,400 pairs from CLWritten and 1,280 pairs from CLSpoken. To ensure sufficient training data for the LSTM, we used1000 pairs of our data for training and the remainder to test
the model. This was done for both CLWritten and CLSpoken.
To allow pretraining to learn representations of the sentences,
we used 50,000 sentences of web-crawled news data from the
IWSLT 2011 Evaluation Campaign [27].

4.4. Evaluation Method
Following the evaluation criteria defined in [29], we investigated the experiments’ results based on two factors:• Compression rate: How much the original sentence
remains after compression
Compression rate is defined as the length of the compressed sentence divided by the original length, where
lower values indicate shorter sentences.
• Accuracy or importance factor: How much of theimportant information is retained from the original.
Accuracy is evaluated using simple string accuracy(SSA) [30], which is based on string edit distance between the compression output generated by the systemand the reference string.

4.2. T3 Baseline Model
As a baseline, we used state-of-the-art tree transduction models [12]. These models use a tree-rewriting process thatchanges the tree from the original parse tree to a compressed
parse tree. Specifically, the model adopts weighted STSGs[6].
Here, two things are learned from the training data: therewrite rules and their weights. The original and compressed
sentences are first aligned word by word, and then the rewriting rules are extracted and their frequency is counted. Basedon the rules and their frequency, the model is then trainedusing support vector machines (SVM) [28]. In these experiments, the tree transduction models are implemented using
the Tree Transduction Toolkit (T3)2 , which includes training
using SVMstruct3 .

5. EXPERIMENTAL RESULTS AND DISCUSSION
In this section, we evaluate the accuracy of the proposed compression method. First, however, we examine the level ofcompression achieved by each method for the various architectures5 . The key solution is to remove as many words as
possible while retaining the important information. Here, thecompression rate of a human annotator is 72%, and we treat
this as the optimum solution.

4.3. RNN Parameters5.1. Examination of Compression Rate
Each hidden layer in the RNN designs uses 100 neurons. During training, the optimum result is obtained with thresholdt = 0.5, where t >= 0.5 is regarded as important and t < 0.5
is regarded as unimportant.
In the input layer, the standard 1-of-|V | word representation is used, in which the i-th word of the vocabulary V
is encoded with a |V |-dimensional vector, where the i-th element is set as 1 and all others are 0. We performed somepreliminary experiments where we used three different varieties of input context when feeding the words into the neural net, including unigram, bigram and trigram. Here, the ngram input vector is a concatenation of n word vectors. For
example, the bigram input vector would be encoded as a 2V dimensional vector, in which the vector is constructed by concatenating unigram word vectors for the current and previous
words. However, the compression rate using unigram is already quite high, and the use of bigram and trigram inputs
further increases the compression rate. This might due to data
sparseness problems. Therefore, we simplified the problemsby only using the unigram type of input.
For every experiment, we use the PyBrain library4 for

LSTM-basic+cont+ext+pae

0.82

LSTM-basic+cont+ext

0.80

LSTM-basic+cont

0.81

LSTM-basic

0.97

Baseline

0.53
0

0.72
Compression rate

Fig. 4. Compression rate of various NN structures: “LSTMbasic,” “LSTM-basic+cont,” “LSTM-basic+cont+ext,” and“LSTM-basic+cont+ext+pae,” in comparison with treetransduction baseline on CLWritten data (note that humanannotator compression rate is 72%).
5 For the neural models, adjusting the threshold parameter can make anarbitrarily short or long compression, with a threshold of 0 resulting in acompression rate of 1 or a threshold of 1 resulting in a compression rate of 0,
but tuning this parameter in detail is beyond the scope of this work

2 T3: http://staffwww.dcs.shef.ac.uk/people/T.Cohn/t3/
3 http://svmlight.joachims.org/svm struct.html
4 PyBrain: http://pybrain.org/

255

Here, we examine the compression rate of networks withvarious structures. Four different structures were evaluated:“LSTM-basic,” “LSTM-basic+cont,” “LSTM-basic+cont+ext,”
and “LSTM-basic+cont+ext+pae” as described in Section 3.
The neural networks were also trained and tested with
CLWritten data. Fig. 4 shows the compression rate of thesevarious RNN structures. For comparison, we also include thebaseline result.
The results show that the T3 baseline tends to over-delete
content, resulting in a quite low compression rate compared tothe compression rate achieved by human annotators. It shouldbe emphasized that, unfortunately, T3 does not provide any
flexibility for user control over varying compression rates, so
the results presented here reflect whatever output T3 generated for a given sentence. The tendency of T3 to produce a
low compression rate has also been discussed by others whenusing T3 for sentence compression [31, 32, 33].
On the other hand, “LSTM-basic” produced a rather highcompression rate, very close to 1. This indicates that the proposed systems provide almost no compression at all. Thismight be due to data sparseness problems. However, the results reveal that the proposed system incorporating a continuous representation layer seems to help handle the sparsenessof the 1-of-|V | representation. The compression rate reaches
81%, which is close to the human annotator’s compression
rate. The results by “LSTM-basic+cont+ext” and “LSTMbasic+cont+ext+pae” seem to yield a similar compression rate
to that of “LSTM-basic+cont.” This shows that the main element influencing the compression rate is the addition of thecontinuous word representation, as opposed to the extra layers
or pretraining.
5.2. Evaluation based on Accuracy
0.8

only our proposed models that provide a compression rate
close to human annotators, which are “LSTM-basic+cont,”“LSTM-basic+cont+ext,” and “LSTM-basic+cont+ext+pae.”
The neural networks are trained and tested with CLWritten
and CLSpoken. For comparison, we also include the baseline
result.
Fig. 5 shows accuracy scores using the various RNN
methods. The results reveal that all proposed systems provide much better accuracy than the baseline; the best one isgiven by “LSTM-basic+cont+ext+pae.” This indicates that
although the pretraining method does not significantly affectthe compression rate, it still contributes to increased performance.
6. CONCLUSIONSIn this study, we investigated the use of LSTM recurrent
networks in performing incremental sentence compression.
In addition, we also analyzed various input layers, RNN
structures, and pretraining methods. The results reveal that
RNN-based sentence compression could obtain compression
output comparable or superior to tree transduction models. The optimal performance was provided by the “LSTMbasic+cont+ext+pae” structure, since it gives a similar compression rate to that of a human annotator and thus improves
the accuracy over the baseline.
At this point, we do not yet take into account the sentence’s grammatical information. In the future, we will incorporate grammatical information and analyze the results withevaluation criteria based on grammaticality. In this way, we
will investigate other structures in order to further compress
sentences while improving accuracy. Furthermore, since ourcurrent investigations are based on the assumption that ASR
is correct, in the future we will investigate the robustness of
our model to ASR errors.

0.7

Accuracy

7. ACKNOWLEDGEMENT

Baseline

0.6
0.5Part of this work was supported by JSPS KAKENHI Grant
Number 26870371.

LSTM-basic+cont

0.4
LSTM-basic+cont+ext

0.3
0.2

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LSTMbasic+cont+ext+pae

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