Modification indices can be requested by adding the argument modindices = TRUE in the summary() call, or by calling the function modindices() directly. The modindices() function returns a data frame, which you can sort or filter to extract what you want. For example, to see only the modification indices for the factor loadings, you can use something like this:

fit <- cfa(HS.model, 
           data = HolzingerSwineford1939)
mi <- modindices(fit)
mi[mi$op == "=~",]
       lhs op rhs     mi    epc sepc.lv sepc.all sepc.nox
25  visual =~  x4  1.211  0.077   0.069    0.059    0.059
26  visual =~  x5  7.441 -0.210  -0.189   -0.147   -0.147
27  visual =~  x6  2.843  0.111   0.100    0.092    0.092
28  visual =~  x7 18.631 -0.422  -0.380   -0.349   -0.349
29  visual =~  x8  4.295 -0.210  -0.189   -0.187   -0.187
30  visual =~  x9 36.411  0.577   0.519    0.515    0.515
31 textual =~  x1  8.903  0.350   0.347    0.297    0.297
32 textual =~  x2  0.017 -0.011  -0.011   -0.010   -0.010
33 textual =~  x3  9.151 -0.272  -0.269   -0.238   -0.238
34 textual =~  x7  0.098 -0.021  -0.021   -0.019   -0.019
35 textual =~  x8  3.359 -0.121  -0.120   -0.118   -0.118
36 textual =~  x9  4.796  0.138   0.137    0.136    0.136
37   speed =~  x1  0.014  0.024   0.015    0.013    0.013
38   speed =~  x2  1.580 -0.198  -0.123   -0.105   -0.105
39   speed =~  x3  0.716  0.136   0.084    0.075    0.075
40   speed =~  x4  0.003 -0.005  -0.003   -0.003   -0.003
41   speed =~  x5  0.201 -0.044  -0.027   -0.021   -0.021
42   speed =~  x6  0.273  0.044   0.027    0.025    0.025

Modification indices are printed out for each nonfree (or nonredundant) parameter. The modification indices are supplemented by the expected parameter change (EPC) values (column epc). The last three columns contain the standardized EPC values (sepc.lv: only standardizing the latent variables; sepc.all: standardizing all variables; sepc.nox: standardizing all but exogenous observed variables)