The automatic text summarization task faces great challenges. The main issue in the area is to identify the most informative segments in the input text. Establishing an effective evaluation mechanism has also been identified as a major challenge in the area. Currently, the mainstream solution is to use deep learning for training. However, a serious exposure bias in training prevents them from achieving better results. Therefore, this paper introduces an extractive text summarization model based on a graph matrix and advantage actor-critic (GA2C) method. The articles were pre-processed to generate a graph matrix. Based on the states provided by the graph matrix, the decision-making network made decisions and sent the results to the evaluation network for scoring. The evaluation network got the decision results of the decision-making network and then scored them. The decision-making network modified the probability of the action based on the scores of the evaluation network. Specifically, compared with the baseline reinforcement learning-based extractive summarization (Refresh) model, experimental results on the CNN/Daily Mail dataset showed that the GA2C model led on Rouge-1, Rouge-2 and Rouge-A by 0.70, 9.01 and 2.73, respectively. Moreover, we conducted multiple ablation experiments to verify the GA2C model from different perspectives. Different activation functions and evaluation networks were used in the GA2C model to obtain the best activation function and evaluation network. Two different reward functions (Set fixed reward value for accumulation (ADD), Rouge) and two different similarity matrices (cosine, Jaccard) were combined for the experiments.
Citation: Senqi Yang, Xuliang Duan, Xi Wang, Dezhao Tang, Zeyan Xiao, Yan Guo. Extractive text summarization model based on advantage actor-critic and graph matrix methodology[J]. Mathematical Biosciences and Engineering, 2023, 20(1): 1488-1504. doi: 10.3934/mbe.2023067
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The automatic text summarization task faces great challenges. The main issue in the area is to identify the most informative segments in the input text. Establishing an effective evaluation mechanism has also been identified as a major challenge in the area. Currently, the mainstream solution is to use deep learning for training. However, a serious exposure bias in training prevents them from achieving better results. Therefore, this paper introduces an extractive text summarization model based on a graph matrix and advantage actor-critic (GA2C) method. The articles were pre-processed to generate a graph matrix. Based on the states provided by the graph matrix, the decision-making network made decisions and sent the results to the evaluation network for scoring. The evaluation network got the decision results of the decision-making network and then scored them. The decision-making network modified the probability of the action based on the scores of the evaluation network. Specifically, compared with the baseline reinforcement learning-based extractive summarization (Refresh) model, experimental results on the CNN/Daily Mail dataset showed that the GA2C model led on Rouge-1, Rouge-2 and Rouge-A by 0.70, 9.01 and 2.73, respectively. Moreover, we conducted multiple ablation experiments to verify the GA2C model from different perspectives. Different activation functions and evaluation networks were used in the GA2C model to obtain the best activation function and evaluation network. Two different reward functions (Set fixed reward value for accumulation (ADD), Rouge) and two different similarity matrices (cosine, Jaccard) were combined for the experiments.
Throughout the paper, we work over an algebraically closed field
Σk=Σk(C,L)⊆Pr |
of
Assume that
σk+1:Ck×C⟶Ck+1 |
be the morphism sending
Ek+1,L:=σk+1,∗p∗L, |
which is a locally free sheaf of rank
Bk(L):=P(Ek+1,L) |
equipped with the natural projection
H0(Bk(L),OBk(L)(1))=H0(Ck+1,Ek+1,)=H0(C,L), |
and therefore, the complete linear system
βk:Bk(L)⟶Pr=P(H0(C,L)). |
The
It is clear that there are natural inclusions
C=Σ0⊆Σ1⊆⋯⊆Σk−1⊆Σk⊆Pr. |
The preimage of
Theorem 1.1. Let
To prove the theorem, we utilize several line bundles defined on symmetric products of the curve. Let us recall the definitions here and refer the reader to [2] for further details. Let
Ck+1=C×⋯×C⏟k+1times |
be the
Ak+1,L:=Tk+1(L)(−2δk+1) |
be a line bundle on
The main ingredient in the proof of Theorem 1.1 is to study the positivity of the line bundle
Proposition 1.2. Let
In particular, if
In this section, we prove Theorem 1.1. We begin with showing Proposition 1.2.
Proof of Proposition 1.2. We proceed by induction on
Assume that
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
is surjective. We can choose a point
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
where all rows and columns are short exact sequences. By tensoring with
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
in which we use the fact that
Since
Lemma 2.1. Let
Proof. Note that
B′/A′⊗A′A′/m′q=B′/(m′qB′+A′)=B′/(m′p+A′)=0. |
By Nakayama lemma, we obtain
We keep using the notations used in the introduction. Recall that
αk,1:Bk−1(L)×C⟶Bk(L). |
To see it in details, we refer to [1,p.432,line –5]. We define the relative secant variety
Proposition 2.2. ([2,Proposition 3.15,Theorem 5.2,and Proposition 5.13]) Recall the situation described in the diagram
αk,1:Bk−1(L)×C⟶Bk(L). |
Let
1.
2.
3.
As a direct consequence of the above proposition, we have an identification
H0(Ck+1,Ak+1,L)=H0(Σk,IΣk−1|Σk(k+1)). |
We are now ready to give the proof of Theorem 1.1.
Proof of Theorem 1.1. Let
b:˜Σk:=BlΣk−1Σk⟶Σk |
be the blowup of
b:˜Σk:=BlΣk−1Σk⟶Σk |
We shall show that
Write
γ:˜Σk⟶P(V). |
On the other hand, one has an identification
ψ:Ck+1⟶P(V). |
Also note that
ψ:Ck+1⟶P(V). |
Take an arbitrary closed point
α−1(x)⊆π−1k(x″)∩β−1k(x′). |
However, the restriction of the morphism
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